>>Welcome back to the cubes coverage of H P S. Green Lake announcement. We've been following Green Lake and the cadence of announcements making. Now we're gonna talk about ransomware, ransomware become a household term. But what people really don't understand is that virtually any bad actor can become a ransomware criminal by going on the dark web hiring a ransomware as a service sticking, putting a stick into a server and taking a piece of the action and that is a really insidious threat. Uh, the adversaries are extremely capable, so we're going to dig into that with Omar assad, who's the storage platform, lead cloud data services at H P E and Deepak verma vice president of product Zito, which is now an H P E company Gentlemen, welcome to the cube. Good to see you. Thank you. >>Thank you. Welcome. Pleasure to be here. So >>over you heard my little narrative upfront. How does the Xarelto acquisition fit into that discourse? >>Thank you. Dave first of all, we're extremely excited to welcome Sir toe into the HP family. Uh, the acquisition of Puerto expands the Green Lake offerings from H P E uh, into the data protection as a service and ransomware protection as a service capabilities and it at the same time accelerates the transformation that the HP storage businesses going through as it transforms itself into more of a cloud native business, which sort of follows on from the May 4th announcements that you helped us cover. Uh, this enables the HP sales teams to now expand the data protection perimeter and to start offering data protection as a service and ransomware as a service with the best in class technologies uh, from a protection site as well as from ransomware recovery side of the house. And so we're all the way down already trying to integrate uh, you know, the little offerings as part of the Green lake offerings and extending support through our services organization. And the more of these announcements are gonna roll out later in the month. >>And I think that's what you want to see from it as a service offering. You want to see a fast cadence of new services that are not a box by a box that are applying. No, it's services that you want to access. So let's, let's talk about before we get into the tech, can we talk about how you're helping customers deal with ransomware? Maybe some of the use cases that you're seeing. >>First of all, extremely excited to be part of the HP family now. Um, Quick history and that we've been around for about 11 years. We've had about 9000 plus customers and they all benefit from essentially the same technology that we invented 11 years ago. First and foremost, one of the use cases has been continuous data protection. So were built on the CdP platform, which means extremely low RTO S and R P O S for recovery. I'll give you example there um, United Airlines is an application that cost them $1 million dollars for every hour that they're down. They use traditional approaches. That would be a lot of loss with Zito, we have that down two seconds of loss in case and the application goes down. So that's kind of core and fundamental to our plaque. The second uh critical use case that for us has been simplicity. A lot of customers have said we make the difficult, simple. So DRS is a complex uh process. Um, give you an example there. Hcea Healthcare Consolidated four different disaster recovery platforms into a single platform in Puerto and saved about $10 million dollars a year. So it's making that operations of having disaster recovery process is much simpler. Um the third kind of critical use case for us as uh, the environment has evolved as the landscape has involved has been around hybrid cloud. So being able to take customers to the platforms that they want to go to that's critical for us And for our customers an example, there is Kingston technology's so Kingston tried some competitive products to move to Azure, it would take them about 24 hours to recover 30 VMS or so with zero technology. They will get about all their 1000 VMS up in Azure instantaneously. So these are three use cases that were foundational. Built. Built the company in the tech. >>Nice. Thank you. Thank you for that. So simple works well these days, especially with all this complexity we have to deal with. Can we get into the secret sauce a little bit. I mean CdP has been around forever. What do you guys do that? That's different. Maybe you can talk about that. Sure. >>Um it's cdp based, I think we've perfected the technology. It's less about being able to just copy the data. It's more about what you do when things go bump. We've made it simpler with driven economies of scale lower and being platform agnostic. We've really brought that up across to whatever platforms once upon a time it was moving from physical to virtual or even across different virtualization platforms and then being able to move across to whatever cloud platform customer may want or or back >>to cbP continuous data protection by the way for the audience that may not know that go ahead. And >>one of the additional points that I want to add to the box comment over here is the the basics of platform independence is what really drew uh hp technologists into the technology because you know, one of the things we have many, we have the high end platform with the H B electra nine Kv of the electro six kids the midrange platform. Then we have a bunch of file and object offerings on the side. What zero does it University universally applies to all those technologies and along with, you know, as you pair them up with our computer offerings to offer a full stack but now the stack is disaster recovery capable. Natively with the integration of certo, you know, one of the things that, you know, Deepak talked about about the as your migrations that a lot of the customers are talking about cloud is also coming up as a D our use case for a lot of our customers, customers, you know, you know, as we went through thousands of customers interviews one of the, one of the key things that came back was investing in a D our data center which is just waiting there for a disaster to happen. It's a very expensive insurance policy. So absurd. Oh, through its native capabilities allows customers to do is to just use public cloud as a D our target and and as a service, it just takes care of all the format conversions and recoveries and although that's completely automated inside the platform and and we feel that, you know, when you combine this either at the high end of data center storage offering or the middle age offering with this replication, D. R. And ransomware protection built into the same package, working under the same hood, it just simplifies and streamlines the customers deployment. >>Come here a couple of things. So first of all historically, if you wanted to recover to appoint within let's say, you know, 10 seconds, five seconds you have to pay up. Big time. Number one. Number two is you couldn't test your D. R. It was too risky. So people just had it in, they had a checkbox on compliance but they actually couldn't really test it because they were afraid they were going to lose data. So it sounds like you're solving both of those problems or >>or you know we remember the D. R. Test where it was a weekend. It was an event right? It was the event and at the end of july that the entire I. T. Organizing honey >>it's not gonna be home this weekend. Exactly what >>we've changed. That is a click of a button. You can D. R. Test today if you want to you can have disaster recovery still running. You can D. R. Test in Azure bring up your environment an isolated network bubble, make sure everything's running and bring it and bring it down. The interesting thing is the technology was invented back when our fear in the industry was losing a data center was losing power was catastrophic, natural disasters. But the technology has lent itself very well to the new threats which which are very much around ransomware as you mentioned because it's a type of disaster. Somebody's going after your data. Physical servers are still around but you still need to go back to a point in time and you need to do that very quickly. So the technology has really just found itself uh appealing to new challenges. >>If a customer asks you can I really eliminate cyber attacks, where should I put my my if I had 100 bucks to spend. Should I spend it on you know layers and defense should I spend it on recovery. Both, what would you tell them? >>I think it's a balanced answer. I think prevention is 100% impossible. Uh It's really I'd say spend it in in thirds. You want to spend a third of it and and prevention a third of it maybe in detection and then a third of it in uh recovery. So it's really that balancing act that means you can't leave the front door open but then have a lot of recovery techniques invested in. It has to be it has to be a balance and it's also not a matter of if it's a matter of when so we invest in all three areas. Hopefully two of them will work to your advantage. >>You dave you you should always protect your perimeter. I mean that that goes without saying but then as you invest in other aspects of the business, as Deepak mentioned, recovery needs to be fast and quick recovery whether from your recovering from a backup disaster. Are you covering from a data center disaster a corrupted file or from a ransomware attack. A couple of things that zero really stitches together like journal based recovery has been allowed for a while but making journal based recovery platform independent in a seamless fashion with the click of a button within five seconds go back to where your situation was. That gives you the peace of mind that even if the perimeter was breached, you're still protected, you know, five minutes into the problem And, and that's the peace of mind, which along with data protection as a service, disaster recovery as a service and now integrating this, you know, recovery from ransomware along with it in a very simple, easy to consume package is what drew us into the >>more you can do this you said on the use the cloud as a target. I could use the cloud as an air gap if I wanted to. It sounds like it's cloud Native, correct? Just wrap your stack in kubernetes and shove it in the cloud and have a host and say we're cloud to No, really I'm serious. So >>absolutely, we we looked at that approach and that that's where the challenge comes in, Right? So I give you the example of Kingston technology just doesn't scale, it's not fast enough. What we did was developed a platform for cloud Native. We consume cloud services where necessary in order to provide that scalability. So one example in Azure is being able to use scale set. So think about a scenario where you just declare a disaster, you've got 1000 VMS to move over, we can spin up the workers that need to do the work to get 1000 VMS spin them down. So you're up and running instantaneously and that involves using cloud Native uh tools and technologies, >>can we stay on that for a minute, So take take us through an example of what life was like would be like without zero trying to recover and what it's like with Puerto resources, complexity time maybe you could sort of paint a picture. Sure. >>Let me, I'll actually use an example from a customer 10 Kata. They uh develop defensive fabrics, especially fabric. So think about firefighters, think about our men and women abroad that need protective clothing that developed the fibers behave. They were hit by ransomware by crypto locker. That this was before zero. Unfortunately it took they took about a two week uh data loss. It took them weeks to recover that environment, bring it back up and the confidence was pretty low. They invested in, they looked at our technology, they invested in the technology and then they were hit with a different variant of crypto locker immediately. The the IT administrators and the ITS folks there were relieved right, they had a sense of confidence to say yes we can recover. And the second time around they had data loss of about 10 seconds, they could recover within a few minutes. So that's the before and after picture giving customers that confidence to say yep, a breach happened, we tried our best but now it's up to recovery and I can recover without having to dig tapes out from some vault and hopefully have a good copy of data sitting there and then try that over and over again and there's a tolerance right before a time before which business will not be able to sustain itself. So what we want to do is minimize that for businesses so that they can recover as quickly as possible with as little data loss as possible. >>Thank you for that. So, Omar, there's a bigger sort of cyber recovery agenda that you have as part of, of green lake, I'm sure. What, what should we expect, what's next? Where do you want to take this? >>So uh excellent question point in the future day. So one of the things that you helped us, uh you know, unveil uh in May was the data services. Cloud console. Data services. Cloud console was the first uh sort of delivery as we took the storage business as it is and start to transform into more of a cloud native business. We introduced electra uh which is the cloud native hardware with the customers buy for persistent storage within their data center. But then data services, cloud console truly cemented that cloud operational model. Uh We separated the management from, from the devices itself and sort of lifted it up as a sas service into the public, public cloud. So now what you're gonna see is, you know, more and more data and data management services come up on the data services. Cloud console and and zero is going to be one of the first ones. Cloud physics was another one that we we talked about, but zero is the is the true data management service that is going to come up on data services, cloud console as part of the Green Lake services agenda that that HP has in the customer's environ and then you're gonna see compliance as a service. You're going to see data protection as a service. You're gonna see disaster recovery as a service. But the beautiful thing about it is, is choice with simplicity as these services get loaded up on data services, clown console. All our customers instantly get it. There's nothing to install, there's nothing to troubleshoot uh, there's nothing to size. All those capabilities are available on the console, customers go in and just start consuming Xarelto capabilities from a management control plane, Disaster recovery control plan are going to be available on the data services, cloud console, automatically detecting electro systems, rian Bear systems, container based systems, whichever our customers have deployed and from there is just a flip of a button. Another way to look at it is it sort of gives you that slider that you have data protection or back up on one side, you've got disaster recovery on one side, you've got ransomware protection on on the extreme right side, you can just move a slider across and choose the service level that you want without worrying about best practices, installation, application integration. All of that just takes control from the data services, cloud concepts. >>Great, great summary because historically you would have to build that right now. You can buy it as a service. You can programmatically, you know, deploy it and that's a game changer. Have to throw it over the fence to some folks. That's okay. Now, you know, make it make it work and then they change the code and you come back a lot of finger pointing. It's now it's your responsibility. >>Absolutely. Absolutely. We're excited to provide Zito continue provides the desert of customers but also integrate with the Green Green Lake platform and let the rest of Green Lake customers experience some of the sort of technology and really make that available as a service. >>That's great. This is a huge challenge for customers. I mean they do, I pay their ransom. Do not pay the ransom. If I pay the ransom the FBI is going to come after me. But if I don't pay the ransom, I'm not gonna get the crypto key. So solutions like this are critical. You certainly see the president pushing for that. The United States government said, hey, we got to do a better job. Good job guys, Thanks for for sharing your story in the cube and congratulations. Thank >>you. Thank you David. >>All right. And thank you for watching everybody. Uh this is the, I want to tell you that everything that you're seeing today as part of the Green Lake announcement is going to be available on demand as part of the HP discover more. So you got to check that out. Thank you. You're watching the cube. >>Mhm mm.

>>we're back, we're assessing the as a service space. H. P. S. Green Lake announcements, my name is Dave balanta, you're watching the cube die on Hinchcliffe is here along with Holger muller, these are the constellation kids, extraordinary analysts guys. Great to see you again. I mean it super experienced. You guys, you deal with practitioners, you deal your technologist, you've been following this business for a long time. Diane, We spoke to Holger earlier, I want to start with you uh when you look at this whole trend to as a service, you see a lot of traditional enterprise companies, hard traditionally hardware companies making that move for for a lot of obvious reasons are they sort of replicating in your view, a market that you know well and sas what's your take on how they're doing generally that trend and how HP is >>operating well. Hp has had a unique heritage. They're coming at the whole cloud story and you know the Hyper Scaler story from a different angle than a lot of their competitors and that's mostly a good thing because most of the world is not yet on the cloud, They actually came from H. P. S original world, their line of servers and networks and so on. Um and and so they bring a lot of credibility saying we really understand the world you live in now but we want to take you to that that as a service future. Uh and and you know, since we understand you so well and we also understand where this is going and we can adapt that to that world. Have a very compelling story and I think that with green like you know, was first started about four years ago, it was off to the side uh you know, with all the other offerings now it's it's really grown up, it's matured a lot and I think you know, as we talked about the announcements, we'll see that a lot of key pieces have fallen into place to make it a very compelling hybrid cloud option for the enterprise. >>Let's talk about the announcement. Was there anything in particular that stood out the move to data management? I think it's pretty interesting is a tam expansion strategy. What's your take on the >>announcement? Well, the you know, the unified analytics uh story I think is really important now. That's the technology piece where they say, they say we can give you a data fabric, you can access your data outside of its silos. It doesn't address a lot of the process and cultural issues around data ownership inside the enterprise, but it's you know, having in the actual platform and as you articulating it as a platform, that's one of the things that was also evident, they were getting better and better at saying this is a hybrid cloud platform and it has all the pieces that you would expect, especially the things like being able to bring your data from wherever it is to wherever people needed to be. Uh you know, that's the Holy Grail, so really glad to see that component in particular. I also like the cloud adoption framework saying we understand how to take you from this parochial world of servers that you have and do a cloud date of hybrid world and then maybe eventually get you get you to a public cloud. We understand all the steps and all the components uh I think that's uh you know, I have a study that fully in depth but it seems to have all the moving parts >>chime in anything stand out to, you >>know, I think it's great announcements and the most important things H. P. S and transformation and when you and transformation people realize who you've been, the old and they're here. Maybe the mass of the new but an experienced technology but I will not right away saying oh it's gonna happen right. It's going to happen like this is gonna be done, it's ready, it's materials ready to use and so on. So this is going to give more data points, more proof points, more capabilities that HB is moving away from whatever they were before. That's not even say that to a software services as a service as you mentioned provider. It's >>been challenging, you look at the course of history for companies that try to go from being a hardware company to a software company, uh HP itself, you know, sort of gave up on that IBM you could say, you know semi succeeded but they've they've struggled what's different >>That will spend 30 billion, >>30 >>four. Exactly. So and of course Cisco is making that transition. I mean every traditional large companies in that transition. What about today? Well, first of all, what do you think about HP es, prospects of doing so? And are there things today in the business that make that, you know more faster, whether it's containers or the cloud itself or just the scale of the internet? >>I mean it's fascinating topic, right? And I think many of the traditional players in the space failed because they wanted to mimic the cloud players and they simply couldn't muster up the Capex, which you need to build up public cloud. Right? Because if you think of the public cloud players then didn't put it up for the cloud offering, they put it up because they need themselves right, amazon is an online retailer google as a search and advertising giant Microsoft is organic load from from from office, which they had to bring to the cloud. So it was easier for them to do that. So no wonder they failed. The good news is they haven't lost much of their organic load. Hp customers are still HP customer service, celebrity security in their own premises and now they're bringing the qualities of the cloud as a service, the pay as you go capabilities to the on premise stack, which helps night leader to reduce complexity and go to what everybody in the post pandemic world wants to get to, which is I only pay for what I use and that's super crucial because business goes up and down. We're riding all the waves in a much, much faster way than ever before. Right before we had seven year cycles, it was kind of like cozy almost now we're down to seven weeks, sometimes seven days, sometimes seven hour cycles. And I don't want to pay for it infrastructure, which was great for how my business was two years ago. I want to pay for it as I use it now as a pivot now and I'm going to use >>Diane. How much of this? Thank you for that whole girl. How much of this is what customers want and need versus sort of survival tactics on the vendors >>part. So I think that there, if you look at where customers want to go, they know they have to go cloud, they had to go as a service. Um, and that they need to make multiple steps to get there. And for the most part, I see green light is being a, a highly credible market response to say, you know, we understand IT better, we helped build you guys up over the last 30 years. We can take you the rest of the way, here's all the evidence and the proof points, which I think a lot of the announcements provide uh, and they're very good on cloud native, but the area where the story, um, you may not be the fullest strength it needs to be is around things like multi cloud. So when I talked to almost any large organization C I O. They have all the clouds need to know, how do I make all this fit together? How do I reconcile that? So for the most part, I think it's closely aligned with actual customer requirements and customer needs. I think these have additional steps to go >>is that, do you feel like that's a a priority? In other words, they got to kind of take a linear path. They got to solve the problem for their core customer base or is it, do you feel like that's not even necessarily an aspiration? And it seems like customers, I want them to go. There is what I'm >>inferring that you're, so I do. Well let's go back to the announcement specifically. So there's there are two great operational announcements, one around the cloud physics and the other one around info site. It gives a wealth of data, you know, full stack about how things are operating, where the needs are, how you might be able to get more efficiencies, how you can shut down silicon, you're not using a lot of really great information, but all that has to live with a whole bunch of other consoles and everybody is really craving the single piece of glass. That's what they want is they want to reduce complexity as holder was saying and say, I want to be able to get my arms around my data center and all of my cloud assets. But I don't want to have to check each cloud. I want it in one place. So uh, but it's great to see those announcements position them for that next step. They have these essential components that are that look, you know, uh, they look best to breed in terms of their capabilities are certainly very modern now. They have to get the rest of that story. >>Hope you were mentioning Capex. I added it up I think last year the big four include Alibaba, spent 100 billion on the Capex and generally the traditional on prem players have been defensive around cloud. Not everything is moving to the cloud, we all know that. But I, I see that as a gift in a way that the companies like HP can build on top of into Diane's point that, you know, extend cross clouds out to the edge, which is, you know, a trillion dollar opportunity, which is just just massive. What are your thoughts on HBs opportunities there and chances of maybe breaking away from the pack >>I think definitely well there's no matter pack left, like there's only 23, it's a triumvirate of maybe it's a good thing from a marketing standpoint. There's not a long list of people who give me hardware in my data center. But I think it increases their chances, right? Like I said, it's a transformation, there's more credibility, there's more data point, there's more usage. I can put more workloads on this. And I see, I also will pay attention to that and look at that for the transformation. No question. >>Yeah. And speaking of C. I. O. S. What are you hearing these days? What's their reaction to this whole trend toward as a service? Do they, do they welcome it? Do they feel like okay it's a wait and see. Uh I need more proof points. What's the sentiment? >>Well, you have to divide the Ceo market basically two large groups. One is the the ones that are highly mature. They tend to be in larger organizations are very sophisticated consumers of everything. They see the writing on the wall and that for most things certainly not everything as a service makes the most sense for all the reasons we know, agility and and and speed, you know, time to value scalability, elasticity, all those great things. Uh And then you have the the other side of the market which they really crave control. They have highly parochial worlds that they've built up um that are hard to move to the cloud because they're so complex and intertwined because they haven't had that high maturity. They have a lot of spaghetti architecture. They're not really ready to move the cloud very quickly. So the the second audience though is the largest one and it's uh you know, the hyper scales are probably getting a lot of the first ones. Um, but the bigger markets, really the second one where the folks that need a lot of help and they have a lot of legacy hardware and software that they need to move and that H P. E understands very well. And so I think from that standpoint they're well positioned to take advantage of an untapped market are relatively untapped market in comparison. Hey, >>in our business we all get pulled in different directions because it would get to eat. But what are some of the cool things you guys are working on in your research that you might want people to know about? >>Uh, I just did a market overview for enterprise application platforms. I'm a strong believer that you should not build all your enterprise software yourself, but you can't use everything that you get from your typical SAs provider. So it's focusing on the extent integration and build capabilities. Bill is very, very important to create the differentiation in the marketplace and all the known sauce players basically for their past. Right? My final example is always to speak in cartoons, right? The peanuts, right? There's Linus of this comfort blanket. Right? The past capability of the SARS player is the comfort blanket, right? You don't fit 100% there or you want to build something strategic or we'll never get to that micro vertical. We have a great enterprise application, interesting topic. >>Especially when you see what's happening with Salesforce and Service now trying to be the platform platforms. I have to check that out. How about >>Diane? Well and last year I had a survey conducted a survey with the top 100 C IOS and at least in my view about what they're gonna do to get through this year. And so I'm redoing that again to say, you know, what are they gonna do in 2022? Because there's so many changes in the world and so, you know, last year digital transformation, automation cybersecurity, we're at the top of the list and it'll be very interesting. Cloud was there too in the top five. So we're gonna see what, how it's all going to change because next year is the year of hybrid work where we're all we have to figure out how half of our businesses are in the office and half are at home and how we're gonna connect those together and what tools we're gonna make, that everybody's trying to figure >>out how to get hybrid. Right, so definitely want to check out that research guys. Thanks so much for coming to the cubes. Great to see you. >>Thanks. Thanks Dave >>Welcome. Okay and thank you for watching everybody keep it right there for more great content from H. P. S. Green Lake announcement. You're watching the cube. Mm this wasn't

>>last decade. The >>major vectors of power in >>tech. We're cloud, mobile >>social and big data. Network computing >>architectures were >>heavily influenced by the mobile leg of that stool with bring your own devices and the SAs >>ification of the enterprise. >>The next 10 years are going to see a focus on instrumented the edge and leveraging architectures that provide a range of capabilities from very small embedded devices, too much larger systems that span hybrid it installations, they move data across clouds and then to the very far edge. >>And is so often the >>case consume arised IOT technology is rapidly driving innovations for enterprise IOT. What are the key trends, challenges and opportunities >>that this >>sea change brings and how should we think about the expanding network >>universe and what will it take to >>thrive in this new environment? Hello everyone. This is Dave Volonte. Welcome back to HPD discovered 2021. You're watching the cubes, virtual coverage of H P. S annual customer event. And with me to discuss the next decade >>of IOT innovation >>and enablement is David Logan, who's the vice president and >>Ceo for the >>Americas for >>HP. Es. Aruba >>networks. David, Welcome to the cube, come on in. >>Thanks so much. It's my pleasure to be here today with you. So >>if the last decade was all about mobile that was legit, it was really driven by the iphone and android adoption and we've been hearing about IOT >>for a long time. >>What's >>the impetus behind the current >>focus on IOT is a >>connected cars, connected homes. What's making it >>real this >>time? From your point of view? >>You know, it's it's really almost everything at once. Uh if you look at how um IOT systems had been developed over the past 10 years, it was super industry specific, A lot of, a lot of mitch implementations, um a lot of product vendors trying to become an IOT platform play. But with all of that innovation that's taken place, it's been additive over the past 10 years. Now. The next 10 years, we're really looking at a phenomenal amount of growth, a phenomenal amount of uh increased innovation to bring IOT solutions to almost any industry for any purpose, whether it's a horizontal need or or a vertical need, that's >>so you guys use terms like solutions, enablement IOT solutions, it's a real big focus of HBs Edge to cloud narrative. I wonder if you could add a little color and some details behind that and explain how Aruba fits in. >>I'll be glad to. So um, H p S Edge to cloud strategy is a really accurate term. Ultimately, the Edge is where IOT solutions are first enabled and it's where data is born, it is where end user experiences live and Aruba's role in Edge to cloud architectures is to provide the connectivity, the performance assurance, the ability to commingle what were once parallel architectures into common infrastructure, common operating platforms and allow this data that's born at the edge to go all the way to the hybrid cloud infrastructure, wherever it needs to go, whether it's an IOT and user application, whether it's an IOT subsystem for industry or or for vertical industry or for vertical enterprise, um the Aruba infrastructure really provides this common operating platform at the edge so that the rest of the enterprise can benefit from what's once transpiring >>when you think about the >>sort of >>candidates for IOT at the enterprise level. I mean, the edge obviously is very fragmented and and of course the big industrial giants, they're on a path there digitizing, they're collecting data, they're driving new monetization initiatives and you know, they got the budgets to do that. Can can smaller companies come to this party. >>Absolutely. And it's really the consumer is ation of IOT that's really driving that. As you mentioned in some of your opening statements, um, the consumer is ation of computing with mobile computing architecture, sas clarification of applications and the extension of the enterprise application environment to the end user with their consumer devices as opposed to their enterprise issue devices. We're seeing the same effects in IOT now, the Consumer Ization of IOT, the release of the amazon echo in 2014, all of the smart tv technology, all of the in home home automation technology that's been developed for individual use cases, for conglomerated use cases. It is this innovation that is now being able to be brought into the enterprise either in the form of pure consumer technology. Just take a look inside your average student dorm room, how much digital technology they brought in, But it's in a it's in an enterprise setting in the university. Uh think about hospitals, health care that have brought in technology to facilitate their particular processes. The consumer is a shin will allow digital experiences to be delivered to the patient in their in their in their treatment suite, for example. So we're gonna see this really drive over the next 10 years quite quite uh quite a significant amount of interesting new use cases. >>Just a quick aside, David, I mean, that Echo example is kind of interesting because when you think about the predominant use cases for AI at the enterprise, it's it's largely modeling that's taking place in the cloud. But when you think about the predominance of AI on whether it's smartphones or you mentioned things like Echo, it's that's kind of a i influencing at the edge, facial recognition is another good example that's bleeding into the enterprise. And it's as you as you know, we've talked about up top it sort of points the way and informs the enterprise, much like the Consumer ization of it. >>Absolutely. Um organizations like Microsoft google amazon, they're really leading the charge from from uh both the Consumer ization perspective but also a developer enablement perspective, bringing the ability for a. I machine learning very specific capabilities. Like you mentioned, video recognition to be able to be brought into enterprise application environments by a developer so that they don't necessarily need to know how to develop that full ai ml stack but can incorporate that capability into their end user applications. And then it's going to lead to brand new productivity innovations that an enterprise can benefit from. Uh It's gonna lead to certainly new business models, it's gonna lead to the ability to integrate um Federated Systems together. Whether it's a business model between two enterprises or whether it's uh the how a particular enterprise operates their own business. It's gonna be, it's gonna be really fascinating. >>I was reading about hand recognition of security. You go beyond fingerprint recognition, it should now be hacked. Let's talk about the market. Everybody talks about the tam, you know, pick your trillion, 1,000,000,001 trillion two trillion. It's a huge total available market, as I said, very fragmented. So how do you think about segmenting the market? How should we think about the different categories of of IOT and solutions and architectures? >>Well, you know, every every organization is easily category categorized by their industry, healthcare, higher education, industrial retail. They all have their particular operating models that generally speaking, have a lot of similarities. And so when we think about market and market segmentation and I think it's first important to think about the particular vertical that enterprise organization belongs to. And then, you know, innovators like like us here in Aruba, we think about how do these particular industries need solutions? And then we look across them for horizontal opportunities, for example, within Aruba's solution set the ability to uh go through rapid iOT device onboarding and security policy process and procedures that's pretty universally applicable across many different industries. But at the same time when you when you look inside a particular vertical, like a heavily industrialized setting, they want to collapse there. OT infrastructure and their I O. T. And I. T. Infrastructure altogether. And they're going to need some very specific solutions to do that. Um, whether it's the ability to guarantee data flow from the edge to the cloud, whether it's security, performance, assurance, whatever their needs, are there going to be very unique to them too. And so looking at it by vertical first is important and then I think sending by size makes sense. And then as we were talking about earlier, the Consumerism nation of IOT systems is really going to bring the ability for medium and smaller organizations to benefit from a lot of these innovations. >>Another another aside maybe it's not a quicker side, but you get the O. T. And the I. T. You know, T. Engineers that are pretty hard core about the way they do things and you got it folks, they have security edicts and compliance and so forth. Kind of how how are they working together? Like who's driving the bus and that >>convergence. You know, every organization has their own operating culture. They have there their prior way of doing things and then they have the future and the real key here for leadership honestly the real key here for organizational leadership, solution, technology leadership in these organizations is to figure out how to bring everybody together the booty uh responsible part of the organization. The folks that are in the line of business, the folks are in biomedical engineering in a health care organization. They know what the end application is, they know what the systems behaviors are going to be from an end user's perspective or from a from a technology perspective as it's applied at the edge, the I. T. Team knows how to build and operate and maintain a bus nature that is all co mingled together is all integrated together. They're going to have to work together so that they understand the end user applications, the experiences that need to be delivered the system's architecture and then how it needs to be operated. But the reason they need to come together is it needs to be using a common enterprise architecture to do so. Common network infrastructure, common computing storage, data platforms at least from a standards perspective, so that the enterprise can get operational efficiency so they can really have the one plus one equals three value proposition moments when multiple systems come together. >>So a couple things we just hit their the organizational challenges, the architectural challenges. You don't want to have more stovepipes? Everybody talks about stovepipes and and data silos. Are there any other challenges that you note that an organization faces in planning and implementing an IOT solutions architecture from your perspective are the organizational, we talked about that. They were talking about some technical and any others that we might have missed, >>you know. Um It's interesting when you look inside at enterprise that has some decent best practices or some good best practices for implementing their their enterprise IOT frameworks. Um as I mentioned, bringing the organization together uh from the end user perspective and the experiences that they need from the operational perspective and the operational technology bleeding into or merging into I. T. Technology. Clearly there's there's that organizational component, but that then needs to map into a newly refined enterprise architecture last decade, you know, the nineties and two thousands, 2010, we talked about enterprise architecture a lot, it was a lot about client server and it was a lot about migrating from legacy application architecture is into next gen and web dato and now it's all about machine to machine and mobile and post mobile. And that means the enterprise architecture that maybe got dusty on the shelf needs to be pulled off and re implemented. And interestingly, as a networking vendor, what we've seen as a best practice is these enterprise organizations recognize that with cloud and mobile and IOT and vendors playing such a such an important role that a lot of control and a lot of visibility has been pulled away from the classic enterprise I. T. Organization and looking at the network as the place where experiences come to uh at the places where uh as to where um instrumentation of the overall end to end architecture can come together. And so they're really now starting to look at the network as as a far more important component than perhaps they did four or five years ago where it might have just been four bars of wifi or connectivity from branch to headquarters. >>When I think about enterprise architectures, I definitely go to workloads like, okay, how is work? How is work that's being done in the enterprise changing and you obviously have a lot of general purpose E R P and financials and Crm and HCM etcetera. You've got this emerging set of workloads that's data intensive, whether it's A I or you know, whatever, whatever you call, some people call matrix workloads, but all the kind of new, interesting, you know, data intensive workloads and then there's a ton of work being done that's just don't even supporting applications directly, it's it's making storage run better or networks run better and so it's kind of wasted cycles if you will. So yeah, I talked a lot of people who are kind of rethinking that architecture to your point based upon the type of work that's being done and obviously things like influencing at the edge that we talked about a little bit earlier, uh are gonna drive that in the enterprise and that's really gonna put new requirements on the architecture, is it not? >>Absolutely. In fact, this is, this is core to the HP edge to cloud strategy and architecture. Ultimately, every organization is going to be different, they have different use cases, different, different business requirements. But um, we are going to find over the next 10 years that a significant amount of the data that is born at the edge and the experiences that are delivered at the Edge need a local presence of computer and communications to enable what needs to, what needs to take place locally from an operations perspective, Let me give you a concrete example. I mentioned health care a couple of times, imagine the healthcare environment of a large healthcare network organization and they need to consume patient telemetry information from all of their patient bedside monitoring systems. At the point at the point of patient care, well, what if the point of patient care is in a hospital tower? What if the point of patient care is in the patient's home? That's a completely different set of circumstances, physically and logically from an enterprise architecture perspective. And so it's particularly important to think through how data will be born at the edge, consumed locally, processed locally. And then forwarded to hybrid cloud computing environments for continued processing after the fact. So you might need to react immediately to some patient telemetry that's collected locally, but then also collect that information processing and the metadata stored somewhere else, maybe maybe haven't diverge into multiple streams? And in all of this, the computing architecture at the edge, the hybrid cloud architecture, the network architecture from edge to cloud all matters because this involves security, involves availability, involves performance, it involves how the data itself is used, the experience of the end users that are responsible for the delivery of the, Of the experience itself. So the ultimate enterprise architecture here is going to evolve yet again. And just as we've seen over 30 years, the centralization, the decentralization, the centralization, the distribution of various functions. We're just we're just seeing that again, because we continue to reinvent how we operate with better and better architectural models, >>right. Pendulums definitely swinging when you, when I think about the compute at the local level, I think it's gonna be super, super high performance and dirt, cheap and low power. Um, and I want to ask you a question about something you said earlier about your strategy is really to look for those horizontal opportunities. So am I right to and for you're not going after the, the deep edge with, you know, specialized capabilities or are you? I think Tesla, right. I mean, you know, designing their own chips for their cars, you're not going there, I presume. But you also reference, hey, there's gonna be some data that's coming back, that's kind of your role. But maybe you can help clarify that for me. >>Yeah, so, so interesting. We are in a way going after the special edge cases, but that's through the creation of an architecture that is malleable enough where you can define an enterprise network architecture and enterprise network experience that will address the horizontal, easy to understand use cases like mobile devices that need wifi connectivity or mobile devices that need bluetooth connectivity or Zig B or what have you. But also we have found that through again through consumer is ation of IOT systems that um, I O T specific technologies for very specific edge use cases are still embedding common access technologies, common networking technologies, common security protocols, um Common orchestration capabilities for compute as some examples. And so what we are building is the ability for uh an enterprise architect or an enterprise network architect to define a single network architecture physically that can commingle lots of different perhaps parallel network architectures into a single common platform and then operate it even though that it might consume multiple, many parallel types of systems ultimately operated as one single entity. Um That honestly, that's the power of the Aruban architecture is even though we have to physically deploy access points and switches and SD WAN gateways to create whatever the enterprise network architecture looks like, It's all driven by software and it's all driven by common interfaces that at some point get down to. Okay, I can actually connect that kind of strange device because it has enough commonality so that I can plug in this USB adapter into this access point. And all of a sudden I've got this connectivity for this very specialized thing transporting specialist protocol across an I. P. Network. So it's um it's really the blend of looking for horizontal opportunities so that we attacked the market effectively but also make sure we don't leave anybody behind in the process just because they've got a specialized need. >>Thank you for that clarification. So room is going to participate in the entire value chain that we've sort of laid out here and visualized. What do you think's going on? Maybe we can talk about the vendor landscape the pretenders from the contenders. What are the keys in your view to the product solutions, the right clarity of vision? Uh maybe some things that haven't been invented yet. How do you how do you think about that? >>Yeah, so um a lot of lessons learned over the past 10 years, I would say um there have been a number of very prominent enterprise technology companies, facilities, tech, um a vertical oriented solutions for healthcare, for industrial settings and they've all at one point or another tried to build a platform strategy, they have decided to self anoint or anoint themselves with, we're going to be the platform for some particular horizontal function inside the enterprise that involves IOT because we want to be the centerpiece where all this data from all these IOT systems concerning this particular environment flows through and we want to help democratize data access. Um Unfortunately most of them still took a very vendor specific point of view about it, even even by layering standards on top of what they've built, um even forming industry consortiums, they haven't necessarily achieved critical mass of what we would all like to see, which is full democratization of IOT solution architectures and IOT data access and I think we're gonna see that over the next 10 years, it's gonna take a while but I think um you know to to your question of what are some interesting uh interesting products or technologies to be developed? Um I think uh industries working together vendors working together like Microsoft like google like amazon like Aruba HP like um in ocean which is an industry consortium, these places where we come together and decide to achieve the greater good to achieve greater benefits for our enterprise customers and build a platform capabilities using standards using open source, using consume arised tech using really critical functions in orchestration, configuration management, aPI architectures, standard standard object models for how how information is communicated. I think that we will be able to democratize IOT data access, I think we'll be able to democratize how IOT systems are deployed and dramatically expand the market opportunity for the benefit of everybody. >>Yeah, we've certainly seen those types of collaborations before, I'm not sure it's ever been this large. Maybe the internet was this large, but that was kind of more government driven than it was a vendor driven, which is your land, give us the bumper sticker for Y H P E in Aruba. >>Well, you know, um HBs in a really um in a really interesting position, we really are enabling the entire edge to cloud architecture, as we've mentioned a few times and the ability to lay out the foundation of the infrastructure for communications for compute for storage regardless of how an enterprise organization wants to consume it, whether it's all at the edge or all in private data centers or in hybrid architecture, whether they want to control the entire architecture top to bottom, whether they want us to help them deploy and manage the architecture on their behalf with industry partners. Ultimately, we are giving them a set of building blocks into end that will coexist with whatever they've already built, help them build a malleable architecture and going forward in the future and really helped them achieve economies of scale, >>David, Very interesting discussion. Thank you so much for your perspectives. Really appreciate you coming on the cube. >>Thank you. Thank you so much. Dave. I really appreciate the time and I'm uh I'm really excited to be part of discover, >>awesome. And thank you for watching this segment of H. P. E. Discovered 2021. You're watching the cube. This is David. Want to keep it right there. Mhm.

>>Mhm Welcome to the cubes coverage of HP discover 2021. I'm your host lisa martin. I've got a couple of guests with me here from emphasis. Alumni Yuma lacks empathy. Is back. Senior vice president and regional head of EMEA emphasis Yuma. It's great to see you welcome back to the program. >>Yeah. Hi Liza. It's great to be back for discover 2021. It's been a great opportunity to meet with health, a lot of our stakeholders and HP. >>Excellent. We're gonna dig into that. And so do Cutie is here as well. The Cto Cloud Advisory, VP hybrid cloud engineering platforms and automation at emphasis. Sergey Welcome to the program. >>Thank you lisa. It's a pleasure to be in the program is my first time but I really enjoy it. Well, >>Welcome. Welcome. So the next 15 minutes or so we're gonna unpack a survey that was just done as we know cloud has catalyzed a lot in the last year. One of those being cloud adoption. Talk to us about some of the things that you've seen as more and more enterprises are moving workloads to cloud. How is a hybrid cloud enabling businesses to grow, enabling them to actually have a competitive edge? >>Uh lisa if you uh if you look at the pre covid scenario and what there are many, many clients which actually made a significant move into cloud, but there were many few, a few of the companies who didn't really take a mature uh cloud adoption. But those companies which actually did the adoption, we see that have taken a big step with the help of the when the covid hit them because they were able to be very resilient. But at the same time they were able to the cloud adoption really help them to improve their business profits. Uh When we did this cloud radar survey across all the geography is we didn't get across the U. S. The latin, the issue pacific the EMEA markets. And when we looked at uh what our clients and enterprises were able to recover and get all of this whole cloud adoption. We've we've got a number of 414 billions of profits that the enterprises can make by using this cloud adoption. And that's what we saw in this survey that we did with our clients. >>Yeah, that's huge enterprises. The survey found can add up to you said 414 billion and that new profits annually through effective cloud adoption and sticking with you for a second. What does emphasis described as effective cloud adoption? >>When we look at cloud adoption, we have enterprises who started shifting workloads which are very comfortable for them. And then uh then they started to take the more mature understanding of moving workloads which were very critical to the business. So when we look at effective, it is a combination of both the ones that were very easy to go to the cloud, the ones that made business is able to bring in new applications and new go to markets uh to their segments to their clients. But then it is also about taking some of those legacy world clothes and making a choice the right choice to take it by transforming those applications and environments uh, into the cloud direction. And that's what we call us effective. It's just not the easy ones but also those complex and legacy rebuild ones that that effectively goes on to transform itself into a new way for the for their clients and for the experience of the users. >>It's a big changes coming, big opportunities. So as we see, we've talked about this for many times, more and more companies moving to multi cloud arrangements for a variety of reasons, what have been some of the things that emphasis has experienced and what are some of your viewpoints on a multi cloud? >>Thank you, lisa. So, um, if you look around >>right, you know, hybrid >>cloud has been the new normal. Right? And um, and if you look at it, private cloud is becoming an essential component for hosting applications. You know, uh you know, when you look at it, it's more about applications which have low latency requirements, it has regulatory requirements or it has a static demand of infrastructure. Now, what emphasis has done in this space is is that, you know, we have um we have developed a framework which we call it as a right cloud solution framework >>and this is >>focused on implementing a hybrid, multi cloud leveraging and in house developed tools and frameworks as well as platforms along with our strategic partner ecosystem, >>that is our biggest contribution >>onto the hybrid multi cloud world. Now, the foundation of our framework is emphasis public cloud platform. It's a unified multi cloud management platform. It can provision, it can orchestrate, it can also manage the cloud deployment across multiple of the environment. It can be a private, it can be public or it can be on the edge. >>Now, apart from all of these >>things, it also offers features and functionalities very similar to the hyper scholars and either it can be in terms of the user experience or it can be in a commercial model or a technology stack or it can be reports or it can be persona based user experience and integration with multiple systems. It brings all of these functionalities >>seamlessly >>across the >>multiple hybrid >>ecosystem protect. That's the biggest contribution from emphasis in this space. >>Got it. Okay. As we see the just clear growth of multi cloud in every industry. Talk to us about what the cloud radar survey uncovered with respective you've mentioned that big number, the correlation between cloud transformation and profitable growth for enterprises across any industry. >>So I did mention about it uh Liza in in the previous question as well. Then we looked at when we look at enterprises trying to take the cloud adoption. The big benefits for the enterprises do happen when they crossed that uh layer of moving a significant part of their existing legacy in a very transformed new world. And that brings in the new way of working for their customers, for their end users and internally as well for their various stakeholders. And that I think is creating a cost structure for them, which is very, very optimal from where they were. But at the same time, it is enabling their ecosystem of of users and customers to come and operate in a very seamless fashion. And that is the biggest advantage of uh boosting profits for them at the same time, cutting costs within the, within the internal stakeholders. So at one stage you're optimizing your cost at another stage, you're bringing in a easiness for your clients to operate on, which is actually creating that enlarged profit boost. >>We're sticking with you for a second. If we unpack that growth, that business profit growth opportunity that the survey uncovered, Are we talking about things like faster time to market, increasing scale? What are some of the things underneath that hood? >>So, if you if you look at uh traditionally cloud was considered uh the enabler for quick, faster time to market. But now cloud has become the central theme for resilience. If you look at the covid pandemic, uh, those, those enterprises which were already cloud enabled, we're able to resiliently and sustain their business and grow their businesses. So as economy started opening up, if I can talk about an automotive client who is today enriching businesses out of china because they have the first economy that has opened up after the pandemic. So you see a lot of enablement for those enterprises which have already taken the cloud journey. And if you look at Today enterprises are in somewhere around 17-18% of of cloud adopt mint and if they can take that to the 40%, that's when they will see that kind of boosted profits. And we can clearly see about $400 plus billion dollars of profits that enterprises can make. >>All right, so let's talk to you for a second. If we look at some of the survey results, the acceleration that is expected to be seen by in the next year of enterprises moving so many more workloads to cloud. You talked about hybrid cloud. Talk to me about how the experience of working with HP in creating joint solution suites is going to help the customers facilitate and drive that transformation. >>Thank you lisa. So if you look at H P E, H P E comes with a fine set of technology and commercial constructs, you know, that complements our right cloud framework >>and they offer >>the solutions. The whole sort of a lot of solutions offer private cloud as a service which is a major component of our right club framework. >>Either it is a >>continuous service with HP is as ephemeral data platform on HP hardware, or >>Vida as a >>service based on a compose Herbal and Converse infrastructure or H P. S cloud built on >>HPC cloud, build on Cray systems >>and all of them commercially supported with an H. P. S. Green leg offering makes it very attractive for our customers. Now, these integrations have helped us in providing a >>very similar >>metering and billing along with the chargeback solutions, very much in line with what is being provided by Hyper scholars. Apart from this, we >>also work very closely with >>H P E >>to create a >>very compelling sourcing strategy for driving hybrid, cloud driven digital transformation while taking cost out and protecting the existing investments through various financial models for our customers, helping them in terms of transforming their digital estate in the, in the new cloud world. >>And um, I want to get your perspective as well, the HP emphasis partnership talk to me about that being a win win for your clients in every industry. >>So actually uh Liza is a great question and this probably is my third uh cube interview and I've told this previously as well in my previous interviews as well. The relationship between emphasis and hedge P. Is very very strategy and it's it's very very top down driven. And today we've seen very high transformative opportunities that two organizations have come together and we won't call it win win but we call it a win win win which is essentially win for HP win for emphasis but even for the clients as well. So if you look at some of the engagements that we have jointly done, everything has been transformative. I can talk about uh energy client where we've done a huge which will V. D. I. Uh engagement with them where we have been able to take them very uh seamlessly when the covid pandemic hit them so that there are significant part of their right to users but be able to operate from their residences. Uh I can talk about a great story about how we had enabled Green Lake for a wind energy company. Uh and how that Green Lake capability help the customer to migrate the application seamlessly uh to a hybrid cloud. And there are so many examples of similar scale and size when we look at clients in the manufacturing space and the automobile sector where we've really done work very closely with PHP across all regions and all geography is uh to make this what I would call when when very partnership. >>I like that when when when who wouldn't want that one more question for you. Talk to me about the next, as we talked about some of those survey results and I think folks can find that survey, the cloud radar survey on the emphasis dot com website. I found it on the homepage there. But looking at how much Transformation is expected in the next 12 months or so, what are some of the things that we can expect from emphasis on H. P. E. to help drive and catalyze that growth that you expect to see in the next 12 months? >>Yeah. And I was talking to you before this interview and you said that yes, we gotta look at this. And I was feeling very happy that you have the opportunity to look at the side. And you said that look there's an opportunity to also make to continuously provide feedback. And we're very happy for clients to come in and look at it and do provide us the feedback. This is a constant learning for us. We have a big learning company Uh and when it comes to uh the next 12 months of agenda, I think the pipeline is very robust for both us and the hp. In terms of the way we want to take proactive transformational opportunities to the to our clients create a value differentiation on the hybrid cloud for them. And uh clearly uh this this survey clearly came back to reflect back to us that our strategy that we've done together as partners is the right strategy because there is a significant headroom for growth uh in the cloud space for both emphasis and H. B. >>Excellent. Well gentlemen, thank you for joining me today, talking to me about what emphasis and HP are doing together, unpacking some of the significant insights that the cloud radar survey has uncovered. We appreciate your time. >>Thank you lisa. Thank you. Thank you for giving us this >>opportunity. Absolutely. For election. Saw ju I'm lisa martin. You're watching the cubes coverage of HP discover 2021. Yeah. Mhm. Yeah.

>>Welcome back to HPD discovered 2021. The virtual edition. My name is Dave a lot and you're watching the cube. We're here with Omar assad is the vice president, GM of H P S H C I and primary storage and data management business. And Sandeep Singh was the vice president of marketing for HP storage division. Welcome gents. Great to see you. >>Great to be here. Dave, >>it's a pleasure to be here today. >>Hey, so uh, last month you guys, you made a big announcement and and now you're, you know, shining the spotlight on that here at discover Cindy. Maybe you can give us a quick recap, what do we need to know? >>Yeah, Dave. We announced that we're expanding HB Green Lake by transforming HB storage to a cloud native software defined data services business. We unveiled a new vision for data that accelerates data dream of transformation for our customers. Uh and it introduced a and we introduced the data services platform that consists of two game changing innovations are first announcement was data services cloud console. It's a SAS based console that delivers the cut operational agility and it's designed to unify data operations through a suite of cloud data services. Our second announcement is H P E electra. It's cloud native data infrastructure to power your data edge to cloud. And it's managed natively with data services cloud console to bring that cloud operational model to our customers wherever their data lives. Together with the data services >>platform. >>Hp Green Green Lake brings that cloud experience to our customers data across edge and on premises environment and lays the foundation for our customers to shift from managing storage to managing data. >>Well, I think it lays the foundation for the next decade. You know, when we entered this past decade, we we we we keep we use terms like software led that that sort of morphed into. So the software defined data center containers with kubernetes, let's zoom out for a minute. If we can homer, maybe you could describe the problems that you're trying to address with this announcement. >>Thanks dave. It's always a pleasure talking to you on these topics. So in my role as general manager for primary storage, I speak with the hundreds of customers across the board and I consistently hear that data is at the heart of what our customers are doing and they're looking for a data driven transformative approach to their business. But as they engage on these things, there are two challenges that they consistently faced. The first one is that managing storage at scale Is rife with complexity. So while storage has gotten faster in the last 20 years, managing a single array or maybe two or three arrays has gotten simpler over time. But managing storage at scale when you deploy fleet, so storage as customers continue to gather, store and life cycle of that data. This process is extremely frustrating for customers. Still I. T. Administrators are firefighting, they're unable to innovate for their business because now data spans all the way from edge to corridor cloud. And then with the advent of public cloud there's another dimension of multi cloud that has been added to their data sprawl. And then secondly what what we what we consistently hear is that idea administrators need to shift from managing storage to managing data. What this basically means is that I. T. Has a desire to mobilize, protect and provision data seamlessly across its lifecycle and across the locations that it is stored at. This ensures that I. D. Leaders uh and also people within the organization understand the context of the data that they store and they operate upon. Yet data management is an extremely big challenge and it is a web of fragmented data silos across processes across infrastructure all the way from test and dev to administration uh to production uh to back up to lifecycle data advantage. Uh And so up till now data management was tied up with storage management and this needs to change for our customers especially with the diversity of the application workloads as they're growing and as customers are expanding their footprint across a multi cloud environment, >>just had to almost um response there. We recently conducted a survey that was actually done by E. S. She. Um and that was a survey of IT. decision makers. And it's interesting what it showcased, 93% of the respondents indicated that storage and data management complexity is impeding their digital transformation. 95% of the respondents indicated that solving storage and data management complexity is a top 10 business initiative for them And 94% want to bring the cloud experience on premises. >>You know, I'll chime in. I think as you guys move to the sort of software world and container world affinity to developers homer. You talked about, you know, things like data protection and we talk about security being bolted on all the time. Now. It's designed in it's it's done at sort of the point of creation, not as an afterthought and that's a big change that we see coming. Uh Let's talk about, you know what also needs to change as customers make the move from this idea of managing storage to to managing data or maybe you can take that one. >>That's a that's a very interesting problem. Right. What are the things that have to be true in order for us to move into this new data management model? So, dave one of the things that the public cloud got right is the cloud operational model which sets the standard for agility and a fast pace for our customers in a classic I. T. On prime model. If you ever wanted to stand up an application or if you were thinking about standing up a particular workload, uh you're going to file a series of I. T. Tickets uh And then you are at the mercy of whatever complex processes exist within organization and and depending on what the level of approvals are within a particular organization, standing up a workload can take days, weeks or even months in certain cases. So what cloud did was a rock that level of simplicity for someone that wanted to instead she ate an app. This means that the provision of underlying infrastructure that makes that workload possible needs to be reduced to minutes from days and weeks. But so what we are intending to do over here is to bring the best of both worlds together so that the cloud experience can be experienced everywhere with ease and simplicity and the customers don't need to change their operating model. So it's blending the two together. And that's what we are trying to usher in into this new era where we start to differentiate between data management and storage management as two independent. Yes, >>Great. Thank you for that. Omer. So deep. I wonder if you could share with the audience, you know, the vision that you guys unveiled, What does it look like? How are you making it actually substantive and and real? >>Yeah. David, That's also great question. Um across the board it's time to reimagine data management. Everything that homer shared. Those challenges are leading to customers needing to break down the silos and complexity that plagues these distributed data environments. And our vision is to deliver a new data experience that helps customers unleash the power of data. We call this vision unified data obs Unified Data Ops integrates data centric policies to streamline data management cloud native control to bring the cloud operational model to where customers data labs and a I driven insights to make the infrastructure invisible. It delivers a new data experience to simplify and bring that agility of cloud to data infrastructure. Streamline data management and help customers innovate faster than ever before. We're making the promise of unified Data Ops Real by transforming H P E storage to a cloud native software defined data services business and introducing a data services platform that expands Hve Green Lake. >>I mean, you know, you talk about the complexity, I see, I look at it as you kind of almost embracing the complexity saying, look, it's gonna keep getting more complex as the cloud expands to the edge on prem Cross cloud, it gets more complex underneath. What you're doing is you're almost embracing that complexity, putting a layer over it and hiding that complexity from from the end customer that and so they can spend their time doing other things over. I wonder if you can maybe talk a little bit more about the data services console, is it sort of another, you know, software layer to manage infrastructure? What exactly is it? >>It's a lot more than that dave and you're you're 100% right. It's basically we're attempting in this release to attack that complexity. Head on. So simply put data services. Cloud console is a SAS based console that delivers cloud operational model and cloud operational agility uh to our customers, it unifies data operations through a series of cloud data services that are delivered on top of this console to our customers in a continuous innovation stream. Uh And what we have done is going back to the point that I made earlier separating storage and data management and putting the strong suites of each of those together into the SAS delivered console for our customers. So what we have done is we have separated data and infrastructure management away from physical hardware to provide a comprehensive and a unified approach to managing data and infrastructure wherever it lives from a customer's perspective, it could be at the edge, it could be in a coal. Oh, it could be in their data center or it could be a bunch of data services that are deployed within the public cloud. So now our customers with data services, cloud console can manage the entire life cycle of their data from all the way from deployment, upgrading and optimizing it uh from a single console from anywhere in the world. Uh This console is designed to streamline data management with cloud data services that enable access to data, It allows for policy-based data protection, it allows for an organizational wide search on top of your storage assets. And we deliver basically a 360° visibility to all your data from a single console that the customer can experience from anywhere. So, so if you look at the journey, the way we're deciding to deliver this. So the first in its first incarnation, uh data services, cloud console gives you infrastructure and cloud data services to start to do data management along with that. But this is that foundation that we are placing in front of our customers, the SAS console through which we get touch our customers on a daily basis. And now as our customers get access to the SAAS platform on the back end, we will continue to roll in additional services throughout the years on a true SAS based innovation base for our customers. And and these services can will be will be ranging all the way from data protection to multiple out data management, all the way to visibility all the way to understanding the context of your data as it's stored across your enterprise. And in addition to that, we're offering a consistent, revised, unified API which allows for our customers to build automation against their storage infrastructure without ever worrying about that. As infrastructure changes. Uh the A P I proof points are going to break for them. That is never going to happen because they are going to be programming to a single SAS based aPI interface from now on. >>Right. And that brings in this idea of infrastructures coding because you talk about as a service to talk about Green Lake and and my question is always okay. Tell me what's behind that. And if and if and if and if you're talking about boxes and and widgets, that's a it's a problem. And you're not you're talking about services and A P. I. S and microservices and that's really the future model. And infrastructure is code and ultimately data as code is really part of that. So, All right. So you guys, I know some of your branding folks, you guys give deep thought uh, to this. So the second part of the announcement is the new product brands and deep maybe you can talk about that a little bit. >>Sure. Ultimately delivering the cloud operational model requires cognitive data infrastructure and that has been engineered to be natively managed from the cloud. And that's why we have also introduced H. P. E. Electra. Omar. Can you perhaps described HB electro even more? >>Absolutely. Thank you. Sandy. Uh, so with with HB Electoral we're launching a new brand of cloud native hardware infrastructure to power our customers data all the way from edge to the core to the cloud. The releases are smaller models for the edge then at the same time having models for the data center and then expanding those services into the public cloud as well. Right. All these hardware devices, Electoral hardware devices are cloud native. Empowered by our Data services. Cloud Council. We're announcing two models with this launch H. P. E. Electra 9000. Uh, this is for our mission critical workloads. It has its history and bases in H P E primera. It comes with 100% availability guarantee. Uh It's the first of its type in the industry. It comes with standard support contract, No special verb is required. And then we're also launching HB electoral 6000. Uh These are based in our history of uh nimble storage systems. Uh These these are for business critical applications, especially for that mid range of the storage market, optimizing price, performance and efficiency. Both of these systems are full envy, any storage powered by our timeless capabilities with data in place upgrades. And then they both deliver a unified infrastructure and data management experience through the data services, cloud console. Uh and and and at the back end, unified ai Ops experience with H P E info site is seamlessly blended in along with the offering for our customers. >>So this is what I was talking about before. It's sort of not your grandfather's storage business anymore. Is this is this is this is something that is part of that, that unified vision, that layer that I talked about. The AP is the program ability. So you're you're reaching into new territory here. Maybe you can give us an example of how the customers experience what that looks like. >>Excellent, loved her Dave. So essentially what we're doing is we're changing the storage experience to a true cloud operational model for our customers. These recent announcements that we just went through along with, indeed they expand the cloud experience that our customers get with storage as a service with HPD Green Lake. So a couple of examples to make this real. So the first of all is simplified deployment. Uh, so I t no longer has to go through complex startup and deployment processes. Now, all you need to do is these systems shipped and delivered to the customer's data center. Operational staff just need to rack and stack and then leave, connect the power cable, connect the network cable. And the job is done from that point onwards, data services console takes over where you can onboard these systems, you can provision these systems if you have a pre existing organization wide security as well as standard profile setup in data services console, we can automatically apply those on your behalf and bring these systems online. From a customer's perspective, they can be anywhere in the world to onboard these systems, they could be driving in a car, they could be sitting on a beach uh And and you know, these systems are automatically on boarded through this cloud operational model which is delivered through the SAAS application for our customers. Another big example. All that I'd like to shed light on is intent based provisioning. Uh So Dave typically provisioning a workload within a data center is an extremely spreadsheet driven trial and error kind of a task. Which system do I land it on? Uh Is my existing sl is going to be affected which systems that loaded, which systems are loaded enough that I put this additional workload on it and the performance doesn't take. All of these decisions are trial and error on a constant basis with cloud data services console along with the electron new systems that are constantly in a loop back information feeding uh Typical analytics to the console. All you need to do is to describe the type of the workload and the intent of the workload in terms of block size S. L. A. That you would like to experience at that point. Data services console consults with intra site at the back end. We run through thousands of data points that are constantly being given to us by your fleet and we come back with a few recommendations. You can accept the recommendation and at that time we go ahead and fully deploy this workload on your behalf or you can specify a particular system and then we will try to enforce the S. L. A. On that system. So it completely eliminates the guesswork and the planning that you have to do in this regard. Uh And last but not the least. Uh you know, one of the most important things is, you know, upgrades has been a huge problem for our customers. Uh And typically oftentimes when you're not in this constant, you know, loop back communication with your customers. It often is a big challenge to identify which release or which bug fix or which update goes on to which particular machine. All of that has been completely taken away from our customers and fully automated. Uh we run thousands of signatures across are installed base. We identify which upgrades need to be curated for which machines in a fleet for a particular customer. And then if it applies to that customer we presented, and if the customer accepts it, we automatically go ahead and upgrade the system and and and last, but not the least from a global management perspective. Now, a customer has an independent data view of their data estate, independent from a storage estate. And data services. Council can blend the two to give a consistent view or you can just look at the fleet view or the data view. >>It's kind of the Holy Grail. I mean I've been in this business a long time and I think I t. People have dreamt about you know this kind of capability for for a long long time. I wonder if we could sort of stay on the customers for a moment here and and talk about what's enabled. Now everybody's talking digital transformation that I joke about the joke. Not funny. The force marched to digital with Covid uh and we really wasn't planned for but the customers really want to drive now that digital transfer some of them are on the back burner and now they're moving to the front burner. What are the outcomes that are that are enabled here? Omar. >>Excellent. So so on on a typical basis for a traditional I. T. Customer, this cloud operational model means that you know information technology staff can move a lot faster and they can be a lot more productive on the things that are directly relevant to their business. They can get up to 99% of the savings back to spend more time on strategic projects or best of all spend time with their families rather than managing and upgrading infrastructure and fleets of infrastructure. Right. For line of business owners, the new experience means that their data infrastructure can be presented can be provision where the self service on demand type of capability. Uh They necessarily don't have to be in the data center to be able to make those decisions. Capacity management, performance management, all of that is died in and presented to them wherever they are easy to consume SAS based models and especially for data innovators, whether it's D B A s, uh whether it's data analysts, they can start to consume infrastructure and ultimately data as a code to speed up their app development because again, the context that we're bringing forward is the context of data decoupling it from. Actually, storage management, storage management and data management are now two separate domains that can be presented through a single console to tie the end to end picture for a customer. But at the end of the day, what we have felt is that customers really really want to rely and move forward with the data management and leave infrastructure management to machine oriented task, which we have completely automated on their behalf. >>So I'm sure you've heard you got the memo about, you know, H H P going all in on as a service. Uh it's clear that the companies all in. How does this announcement fit in to that overall mission, Sandeep >>Dave. We believe the future is edge to cloud and our mission is to be the edge to cloud platform as a service company and as as HB transforms HP Green Lake is our unified cloud platform. Hp Green Link is how we deliver cloud services and agile cloud experiences to customers, applications and data across the edge to cloud. With the storage announcement that we made recently, we announced that we're expanding HB Green Lake with as a service transformation of the HPV storage business to a cloud native software defined data services business. And this expands storage as a service delivering full cloud experience to our customers data across edge and on prem environment across the board were committed to being a strategic partner for every one of our customers and helping them accelerate their digital transformation. >>Yeah, that's where the puck is going guys. Hey as always great conversation with with our friends from HP storage. Thanks so much for the collaboration and congratulations on the announcements and I know you're not done yet. >>Thanks. Dave. Thanks. Dave. All right. Dave. It's a pleasure to be here. >>You're very welcome. And thank you for being with us for hp. You discovered 2021. You're watching the cube, the leader digital check coverage. Keep it right there, but right back. >>Mhm. Mhm.

>>Please >>welcome back to HPD discovered 2021. The cubes virtual coverage, continuous coverage of H P. S H. P. S. Annual customer event. My name is Dave Volonte and we're going to dive into the intersection of high performance computing data and AI with DR Eng limb go who is the senior vice president and CTO for AI Hewlett Packard enterprise Doctor go great to see you again. Welcome back to the cube. >>Hello Dave, Great to talk to you again. >>You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the day two keynotes here at discover and you talked about thriving in the age of insights and how to craft a data centric strategy. And you addressed you know some of the biggest problems I think organizations face with data that's You got a data is plentiful but insights they're harder to come by. And you really dug into some great examples in retail banking and medicine and health care and media. But stepping back a little bit with zoom out on discovered 21, what do you make of the events so far? And some of your big takeaways? >>Mm Well you started with the insightful question, Right? Yeah, data is everywhere then. But we like the insight. Right? That's also part of the reason why that's the main reason why you know Antonio on day one focused and talked about that. The fact that we are now in the age of insight, right? Uh and uh and and how to thrive thrive in that in this new age. What I then did on the day to kino following Antonio is to talk about the challenges that we need to overcome in order in order to thrive in this new asia. >>So maybe we could talk a little bit about some of the things that you took away in terms I'm specifically interested in some of the barriers to achieving insights when customers are drowning in data. What do you hear from customers? What we take away from some of the ones you talked about today? >>Oh, very pertinent question. Dave You know the two challenges I spoke about right now that we need to overcome in order to thrive in this new age. The first one is is the current challenge and that current challenge is uh you know stated is no barriers to insight. You know when we are awash with data. So that's a statement. Right? How to overcome those barriers. What are the barriers of these two insight when we are awash in data? Um I in the data keynote I spoke about three main things. Three main areas that received from customers. The first one, the first barrier is in many with many of our customers. A data is siloed. All right. You know, like in a big corporation you've got data siloed by sales, finance, engineering, manufacturing, and so on, uh supply chain and so on. And uh there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the that was the first barrier. We spoke about barriers to incite when we are washed with data. The second barrier is uh that we see amongst our customers is that uh data is raw and dispersed when they are stored and and uh and you know, it's tough to get tough to to get value out of them. Right? And I in that case I I used the example of uh you know the May 6 2010 event where the stock market dropped a trillion dollars in in tens of minutes. You know, we we all know those who are financially attuned with know about this uh incident, But this is not the only incident. There are many of them out there and for for that particular May six event, uh you know, it took a long time to get insight months. Yeah, before we for months we had no insight as to what happened, why it happened, right. Um, and and there were many other incidences like this and the regulators were looking for that one rule that could, that could mitigate many of these incidences. Um, one of our customers decided to take the hard road to go with the tough data right? Because data is rolling dispersed. So they went into all the different feeds of financial transaction information, took the took the tough took the tough road and analyze that data took a long time to assemble. And they discovered that there was quote stuffing right? That uh people were sending a lot of traits in and then cancelling them almost immediately. You have to manipulate the market. Um And why why why didn't we see it immediately? Well, the reason is the process reports that everybody sees the rule in there that says all trades, less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 103 100 100 shares trades uh to fly under the radar to do this manipulation. So here is here the second barrier right? Data could be raw and dispersed. Um Sometimes you just have to take the hard road and um and to get insight And this is 1 1 great example. And then the last barrier is uh is has to do with sometimes when you start a project to to get insight to get uh to get answers and insight. You you realize that all the datas around you but you don't you don't seem to find the right ones to get what you need. You don't you don't seem to get the right ones. Yeah. Um here we have three quick examples of customers. 111 was it was a great example right? Where uh they were trying to build a language translator, a machine language translator between two languages. Right? By not do that. They need to get hundreds of millions of word pairs, you know, of one language compared uh with a corresponding other hundreds of millions of them. They say, well I'm going to get all these word pairs. Someone creative thought of a willing source. And you thought it was the United Nations, you see. So sometimes you think you don't have the right data with you, but there might be another source. And the willing one that could give you that data Right? The 2nd 1 has to do with uh there was uh the uh sometimes you you may just have to generate that data, interesting one. We had an autonomous car customer that collects all these data from their cars, right? Massive amounts of data, loss of sensors, collect loss of data. And uh, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car uh in in um in fine weather and collected the car driving on this highway in rain and also in stone, but never had the opportunity to collect the car in hill because that's a rare occurrence. So instead of waiting for a time where the car can dr inhale, they build a simulation you by having the car collector in snow and simulated him. So, these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated the fact that data silo the Federated it various associated with data. That's tough to get that. They just took the hard road, right? And, and sometimes, thirdly, you just have to be creative to get the right data. You need, >>wow, I I'll tell you, I have about 100 questions based on what you just said. Uh, there's a great example, the flash crash. In fact, Michael Lewis wrote about this in his book The Flash Boys and essentially right. It was high frequency traders trying to front run the market and sending in small block trades trying to get on the front end it. So that's and they, and they chalked it up to a glitch like you said, for months. Nobody really knew what it was. So technology got us into this problem. I guess my question is, can technology help us get out of the problem? And that maybe is where AI fits in. >>Yes, yes. Uh, in fact, a lot of analytics, we went in to go back to the raw data that is highly dispersed from different sources, right, assemble them to see if you can find a material trend, right? You can see lots of trends, right? Like, uh, you know, we if if humans look at things right, we tend to see patterns in clouds, right? So sometimes you need to apply statistical analysis, um math to to be sure that what the model is seeing is is real. Right? And and that required work. That's one area. The second area is uh you know, when um uh there are times when you you just need to to go through that uh that tough approach to to find the answer. Now, the issue comes to mind now is is that humans put in the rules to decide what goes into a report that everybody sees. And in this case uh before the change in the rules. Right? But by the way, after the discovery, uh authorities change the rules and all all shares, all traits of different any sizes. It has to be reported. No. Yeah. Right. But the rule was applied uh you know, to say earlier that shares under 100 trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and and under for understandable reasons. I mean they probably didn't want that for various reasons not to put everything in there so that people could still read it uh in a reasonable amount of time. But uh we need to understand that rules were being put in by humans for the reports we read. And as such there are times you just need to go back to the raw data. >>I want to ask, >>it's gonna be tough. >>Yeah. So I want to ask a question about AI is obviously it's in your title and it's something you know a lot about but and I want to make a statement, you tell me if it's on point or off point. So it seems that most of the Ai going on in the enterprise is modeling data science applied to troves of data but but there's also a lot of ai going on in consumer whether it's you know, fingerprint technology or facial recognition or natural language processing will a two part question will the consumer market as has so often in the enterprise sort of inform us uh the first part and then will there be a shift from sort of modeling if you will to more you mentioned autonomous vehicles more ai influencing in real time. Especially with the edge you can help us understand that better. >>Yeah, it's a great question. Right. Uh there are three stages to just simplify, I mean, you know, it's probably more sophisticated than that but let's simplify three stages. All right. To to building an Ai system that ultimately can predict, make a prediction right or to to assist you in decision making, have an outcome. So you start with the data massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data and the machine uh starts to evolve a model based on all the data is seeing. It starts to evolve right to the point that using a test set of data that you have separately kept a site that you know the answer for. Then you test the model uh you know after you trained it with all that data to see whether it's prediction accuracy is high enough and once you are satisfied with it, you you then deploy the model to make the decision and that's the influence. Right? So a lot of times depend on what what we are focusing on. We we um in data science are we working hard on assembling the right data to feed the machine with, That's the data preparation organization work. And then after which you build your models, you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you you pick one model and the prediction isn't that robust, it is good but then it is not consistent right now. What you do is uh you try another model so sometimes it's just keep trying different models until you get the right kind. Yeah, that gives you a good robust decision making and prediction after which It is tested well Q eight. You would then take that model and deploy it at the edge. Yeah. And then at the edges is essentially just looking at new data, applying it to the model that you have trained and then that model will give you a prediction decision. Right? So uh it is these three stages. Yeah, but more and more uh your question reminds me that more and more people are thinking as the edge become more and more powerful. Can you also do learning at the edge? Right. That's the reason why we spoke about swarm learning the last time, learning at the edge as a swamp, right? Because maybe individually they may not have enough power to do so. But as a swamp they made >>is that learning from the edge? You're learning at the edge? In other words? >>Yes. >>Yeah, I understand the question. Yeah. >>That's a great question. That's a great question. Right? So uh the quick answer is learning at the edge, right? Uh and and also from the edge, but the main goal, right? The goal is to learn at the edge so that you don't have to move the data that the edge sees first back to the cloud or the core to do the learning because that would be the reason. One of the main reasons why you want to learn at the edge, right? Uh So so that you don't need to have to send all that data back and assemble it back from all the different Edge devices, assemble it back to the cloud side to to do the learning right. With someone you can learn it and keep the data at the edge and learn at that point. >>And then maybe only selectively send the autonomous vehicle example you gave us great because maybe there, you know, there may be only persisting, they're not persisting data that is inclement weather or when a deer runs across the front. And then maybe they they do that and then they send that smaller data set back and maybe that's where it's modelling done. But the rest can be done at the edges. It's a new world that's coming down. Let me ask you a question, is there a limit to what data should be collected and how it should be collected? >>That's a great question again, you know uh wow today, full of these uh insightful questions that actually touches on the second challenge. Right? How do we uh in order to thrive in this new age of insight? The second challenge is are you know the is our future challenge, right? What do we do for our future? And and in there is uh the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that I talk about what to collect right? When to organize it when you collect and where will your data be, you know, going forward that you are collecting from? So what, when and where for the what data for the what data to collect? That? That was the question you ask. Um it's it's a question that different industries have to ask themselves because it will vary, right? Um Let me give you the, you use the autonomous car example, let me use that. And We have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from the fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars, collecting data so they can train and eventually deploy commercial cars. Right? Um, so this data collection cars they collect as a fleet of them collect 10 petabytes a day and when it came to us uh building a storage system yeah, to store all of that data, they realized they don't want to afford to store all of it. Now here comes the dilemma, right? Should what should I after I spent so much effort building all these cars and sensors and collecting data, I've now decide what to delete. That's a dilemma right now in working with them on this process of trimming down what they collected. You know, I'm constantly reminded of the sixties and seventies, right? To remind myself 16 seventies we call a large part of our D. N. A junk DNA. Today we realize that a large part of that what we call john has function as valuable function. They are not jeans, but they regulate the function of jeans, you know? So, so what's jumped in the yesterday could be valuable today or what's junk today could be valuable tomorrow. Right? So, so there's this tension going on right between you decided not wanting to afford to store everything that you can get your hands on. But on the other hand, you you know, you worry you you you ignore the wrong ones, right? You can see this tension in our customers, right? And it depends on industry here. Right? In health care, they say I have no choice. I I want it. All right. One very insightful point brought up by one health care provider that really touched me was, you know, we are not we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But you also care for the people were not caring for. How do we find them? Mhm. Right. And that therefore they did not just need to collect data that is uh that they have with from their patients. They also need to reach out right to outside data so that they can figure out who they are not caring for. Right? So they want it all. So I tell us them. So what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and find out. Of course they also come back to us rightfully that, you know, we have to then work out a way to help them build that system, you know, so that health care, right? And and if you go to other industries like banking, they say they can't afford to keep them on, but they are regulated. Seems like healthcare, they are regulated as to uh privacy and such. Like so many examples different industries having different needs but different approaches to how what they collect. But there is this constant tension between um you perhaps deciding not wanting to fund all of that uh all that you can stall right on the other hand, you know, if you if you kind of don't want to afford it and decide not to store some uh if he does some become highly valuable in the future right? Don't worry. >>We can make some assumptions about the future, can't we? I mean, we know there's gonna be a lot more data than than we've ever seen before. We know that we know. Well notwithstanding supply constraints on things like nand, we know the prices of storage is gonna continue to decline. We also know and not a lot of people are really talking about this but the processing power but he says moore's law is dead. Okay, it's waning. But the processing power when you combine the Cpus and N. P. U. S. And Gpus and accelerators and and so forth actually is is increasing. And so when you think about these use cases at the edge, you're going to have much more processing power, you're going to have cheaper storage and it's going to be less expensive processing. And so as an ai practitioner, what can you do with that? >>So the amount of data that's gonna come in, it's gonna we exceed right? Our drop in storage costs are increasing computer power. Right? So what's the answer? Right? So so the the answer must be knowing that we don't and and even the drop in price and increase in bandwidth, it will overwhelm the increased five G will overwhelm five G. Right? Given amount 55 billion of them collecting. Right? So the answer must be that there might need to be a balance between you needing to bring all that data from the 55 billion devices data back to a central as a bunch of central. Cause because you may not be able to afford to do that firstly band with even with five G. M and and SD when you'll still be too expensive given the number of devices out there, Were you given storage costs dropping? You'll still be too expensive to try and store them all. So the answer must be to start at least to mitigate the problem to some leave both a lot of the data out there. Right? And only send back the pertinent ones as you said before. But then if you did that, then how are we gonna do machine learning at the core and the cloud side? If you don't have all the data, you want rich data to train with. Right? Some sometimes you wanna mix of the uh positive type data and the negative type data so you can train the machine in a more balanced way. So the answer must be eventually right. As we move forward with these huge number of devices out of the edge to do machine learning at the edge today, we don't have enough power. Right? The edge typically is characterized by a lower uh energy capability and therefore lower compute power. But soon, you know, even with lower energy they can do more with compute power, improving in energy efficiency, Right? Uh So learning at the edge today we do influence at the edge. So we data model deploy and you do in France at the age, that's what we do today. But more and more I believe given a massive amount of data at the edge, you, you have to have to start doing machine learning at the edge and, and if when you don't have enough power then you aggregate multiple devices, compute power into a swamp and learn as a swan. >>Oh, interesting. So now of course, if, if I were sitting and fly, fly on the wall in hp board meeting, I said okay. HB is as a leading provider of compute how do you take advantage of that? I mean we're going, we're, I know its future, but you must be thinking about that and participating in those markets. I know today you are, you have, you know, edge line and other products. But there's, it seems to me that it's, it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that >>opportunity for the customers? The world will have to have a balance right? Where today the default? Well, the more common mode is to collect the data from the edge and train at uh at some centralized location or a number of centralized location um going forward. Given the proliferation of the edge devices, we'll need a balance. We need both. We need capability at the cloud side. Right? And it has to be hybrid and then we need capability on the edge side. Yeah. That they want to build systems that that on one hand, uh is uh edge adapted, right? Meaning the environmentally adapted because the edge different. They are on a lot of times. On the outside. Uh They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery power. Right? Um, so you have to build systems that adapt to it. But at the same time they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run a rich set of applications. So yes. Um that's that's also the insightful for that Antonio announced in 2018 Uh the next four years from 2018, right $4 billion dollars invested to strengthen our edge portfolio. Edge product lines, Right. Edge solutions. >>I can doctor go, I could go on for hours with you. You're you're just such a great guest. Let's close. What are you most excited about in the future? Of of of it. Certainly H. P. E. But the industry in general. >>Yeah. I think the excitement is uh the customers, right? The diversity of customers and and the diversity in a way they have approached their different problems with data strategy. So the excitement is around data strategy, right? Just like you know uh you know, the the statement made was was so was profound, right? Um And Antonio said we are in the age of insight powered by data. That's the first line, right. Uh The line that comes after that is as such were becoming more and more data centric with data, the currency. Now the next step is even more profound. That is um You know, we are going as far as saying that you know um data should not be treated as cost anymore. No. Right. But instead as an investment in a new asset class called data with value on our balance sheet, this is a this is a step change right? In thinking that is going to change the way we look at data, the way we value it. So that's a statement that this is the exciting thing because because for for me, a city of Ai right uh machine is only as intelligent as the data you feed it with data is a source of the machine learning to be intelligent. So, so that's that's why when when people start to value data, right? And and and say that it is an investment when we collect it, it is very positive for AI because an AI system gets intelligent, get more intelligence because it has a huge amounts of data and the diversity of data. So it would be great if the community values values data. Well, >>you certainly see it in the valuations of many companies these days. Um and I think increasingly you see it on the income statement, you know, data products and people monetizing data services and maybe eventually you'll see it in the in the balance. You know, Doug Laney, when he was a gardener group wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? Dr >>yeah. Question is is the process and methods evaluation right. But I believe we'll get there, we need to get started and then we'll get there. Believe >>doctor goes on >>pleasure. And yeah. And then the Yeah, I will well benefit greatly from it. >>Oh yeah, no doubt people will better understand how to align you know, some of these technology investments, Doctor goes great to see you again. Thanks so much for coming back in the cube. It's been a real pleasure. >>Yes. A system. It's only as smart as the data you feed it with. >>Excellent. We'll leave it there, thank you for spending some time with us and keep it right there for more great interviews from HP discover 21 this is Dave Volonte for the cube. The leader in enterprise tech coverage right back

>>mm >>Welcome back to HPD discovered 2021. The virtual version of the show. My name is Dave valentin. You're watching the cube we're here with David Harvey is the vice president of strategic alliances at VM. David. Good to see you. How you doing? >>I'm well thanks David yourself you've been good, >>yep. Doing great thank you. Hey you've heard the term follow the money, we're gonna follow the data. How >>about right >>So HP and wien you're celebrating a 10 year milestone in your alliance. That's a lot of good parties at at the HP discover shows. And uh of course we miss miss being face to face this year but next year we'll be back rocket but uh talk a little bit about what that milestone means to you. >>Yeah Thanks. Dave. And you're right. It is a milestone. I mean when you look at alliances or partnerships overall, it's crazy that you can maintain this depth of partnership is depth of relationship and this success for 10 years. I mean H. P. Was our number one alliance that we started working with when we started being X Number of years ago. Um and the reason for that was that we really came together from the very start with a philosophy about the approach we wanted to provide to the customer and also the synergy of technology. Um and 10 years is a long time. I mean how many alliances that you've seen in the industry Um that have managed to maintain for 10 years and we're stronger than ever as we come into this point and that's amazing. So from that point of view we're really excited for this 10 year milestone. We're really pleased that the investment from both sides as maintained and grown through that time period. Um And as you said it's a shame we're not doing this in person but this is a great event for us and that's why we're so proud to be top sponsor this year and supporting the charge for this government. >>Well, congratulations on that milestone immunity. So often when I talk to folks that are in your role, they'll complain and yeah, we do it. We have a lot of numbers, but not a lot of hard y and not a lot of fruitful partnerships and they'll do barney deals. I love you, you love me, you will do a press release but it's not driving and I happen to know that the HPV in relationship is very productive and I think, you know, one of the key moves when when HP split itself into it took its competitive data protective product that sold that off and then that just opened up a whole new opportunity for the relationships. It was a game changer. So but looking back, what do you think was the meaningful sort of investment that the alliance has really made together? >>Yeah, great question. And it's a really cheesy answer, but it's it's one of those very rare scenarios, where is the truth and his death? You know, the depth of discussion from the very start was really what built that foundation, We were the launch back up part of the three part, um, and every release team has done since then has had a key HP component to it. And more importantly, as you said as HP has evolved through that period, the divestiture and the overall movement of their portfolio. We've continued to listen to each other on what is important to both parties. But while that's great from the relationship and the alliance, the one thing that's never changed is the response of the customer to saying, not only have you integrated together on technology, you've unified your message, you provide a supply chain that is meaningful to my business by simplifying and providing value and you continue to evolve. You continue to adjust and move as you've gone through the time period in our needs have changed. I mean we started with servers, we worked with storage, we're with green labour? S moral like all across that portfolio. We found a way to continue to listen to each other and what's important and that's been killed. >>So what are the waves that you're you're surfing here, You put on the binoculars and look forward what are going to be the most important areas that you guys invest in and focus on in the future? >>Yeah. Great question. I mean we're focused on three things for the for the medium to short term here and looking at there is rapidly recovering your data. You know, the news at the moment is exploding related to issues companies are having, which is so unfortunate and recovering data quickly. It's an economic component is not just about the ability to do it fast, it's about the fact that the quicker you bring data back in this circumstance where you have to, the better it is for your bottom line. We also simplify that data protection. And the reason for that is that if you look at the diversity of the portfolio, HP has you want unification regardless of what products you're buying from HP, you want to make sure that you're working with solutions that work with all of those different parts of it. As I mentioned, service storage as moral Green Lake et cetera. And so that simplification of data protection is huge. And finally it's getting your data protection as a service. We've been working with Green Lake for a good number of years now and it's one of the fastest growing areas of our partnership. But if you bring those three things together, the customers are deciding that modern data protection needs, that they have, they're looking at the hybrid world, they're looking at all parts of the portfolio from the thought leaders, they work with specifically HP and they're wanting to make sure that they've got that unification moving forward and that whatever decisions they make with the infrastructure, the underlying protection of their data continues to be a core component that they can evolve with as they move their needs forward. >>We'll talk about that speedy recovery. There's so much in the news today, we're seeing all this, all this ransomware. I mean it's bringing down organizations, it's affecting supply chains all over the world very concerning. And there's two dimensions here. One is the speed to recover. We can all relate when, you know, when your laptop freezes like, oh, I gotta reboot and it takes five minutes and you're frustrated. Imagine your whole business, you know, it takes half a day to recover. That's huge. The other dimension, of course, is how much data you you lose in that recovery and you try to compress that RP. Oh right. Is as tight as possible. And that's the other sort of value that that customers look for from a combination of HPV and VM. So, but I want to ask you, so we're here at HP covering HP discover you can't talk about hB without getting a kool aid injection of Green Lake and as a service. And how are you guys sort of addressing those as a service needs for today's customers? >>Yeah, it's a great question. And by the way kudos, you can be a salesperson force with our pos and all those keywords. I love it. But what I would say overall is that when you look at the changing way customers are spending, um it depends on where they're structuring their financial desires, whether it's the Capex world, the optics world etcetera. And Green Led by its nature allows you to look at having the control of a physical component. But having the economic structure of in some respects pay as you go when you look at it in that component. And so you're avoiding that capital investment concern. But you're getting the power and the strength of the management component as well. And that's what's really important. I mean when you look at overall movement, Yes, you did a really interesting report recently and they're saying that spending on data center protection is going to grow 50% this year in 2021. Looking at improving that level of key component for their data centers as they go through that modernization. And so from that point of view, what we're seeing and this is applicable for HP more than anybody else. Is that the speed that they came out with the Green Lake a number of years ago allowed customers, especially the big enterprises, we're having a massive amount of success together, enabled them to decide the economic buying model that they wanted and to combine that with the best of breed service and management and control. So from our point of view, you know, that's something we've been investing within a long period of time now, not only on the solutions but also on how we go to market together. Our field team is working very closely with their field team within Green Lake to be there so that the customer can utilize it as a tool and not feel like they're having a different conversation because we're so baked in with the rest of the organization. So from our point of view, Green Lake is key to how things are moving forward and other things that the storage departments doing as well as they look at some of their new ways with their announcements we've, they've recently made with buying down on demand and new products they're having. So it's allowing the customer to have that choice and from us, it forms a core component of how we're working together. However you decide you want to consume the HP portfolio. You should have the ability for us to seamlessly work with it. And to your point, that's why that growth rate on our oi but more importantly on the revenue and the amount of growth of our customers year over year have really embraced that synchronization together. >>David, I think of your thoughts on containers. Generally I want to I want to talk about the cast and acquisition specifically but I want to ask about it in the context of the two things. One is just kind of the overall where you see that going and and how you're working with H. P. E. On that. But the other is as it relates to two of the most vexing problems for I. T. Folks in the past have been been security and data protection and their their their adjacency is you're not a security company but it's a kind of a cousin if you will. And and both of those areas have always been an afterthought after you get snake bitten, you close the barn door kind of thing and it's a bolt on. Okay. I got my application it's all hard and I got my database and ready to go, oh hey how do we back this thing up as an afterthought when I think containers and and and I think kubernetes I think developers I think infrastructure as code and now you're designing in security and data protection focusing on the ladder obviously. How does the cast and acquisition and what H. P. S doing on containers fit into that context and how do you see it evolving overall? >>Yeah that's a great question and there's two pastoring. I mean if you look at the way that HP moves to market and you look at the themes and the focus they've had now for the last three plus years with regard to that data center transformation and the movement and modernization of it. This has been a part of it But as you exactly said, this is a new type of context point has come in. Obviously we acquired casting as you alluded to early in 2020 because for us we absolutely believe that this is a core component righty. And you raised the point perfectly there Dave it used to be a component after you're snakebit, it's not today. I mean you alluded to it with regard to what's going on in the news over the last few weeks or so. It's nowhere near an afterthought Now it's a component that's built in from the start and that's why when you look at some of those studies about the spend in this area overall it isn't an afterthought anymore but I agree with you, it was when you look back a number of years and so for us casting build a very key area of our portfolio but it also allowed us with HP to double down on another area of investment for themselves. Esmeralda is a key play for HP moving forward. You can get casting on the Admiral marketplace and that's another example, as I was saying, it doesn't matter how you keep evolving your relationship with HP, how you keep drawing down from the portfolio, you want to make sure that the data protection, you've got the simplified data protection across all of these areas, is there from the start? And what we're finding is with Greenfield sites, with new applications with new deployments where containers kubernetes really comes into play. They are looking to buy it together at the start so that they can focus on learning, acquiring deploying and really maximizing the benefit of kubernetes and not worry about that snakebite component you talked about. So for us, you know, it supports our portfolio and it allows us to stay with HP as they continue to evolve their strategy. >>That SG Stat of 50% growth in data protection is pretty amazing and it's funny, I think back to the insight acquisition uh VM and you know, conventional wisdom would have said, oh wow, what a bummer. They bought this thing right before a global pandemic in an economic downturn. It's but in this, in your businesses like real estate with pre pandemic post pandemic evaluation should be skyrocketing is as a function of of the heightened focus on digital and security and data protection. So it's really an exciting time. Um if I were to ask you this question 10 years ago where where HP envy emceeing joint success in the marketplace? It would have been, well of course, virtualization, it's all the >>rage. Where >>are you seeing success today? >>And that's a great question and it's interesting you talk about it with the pandemic. I'll be honest, the last recession us that I was in the digital messaging market and at that point when economies get tight, everybody invest in cheaper types of marketing, which is digital messaging. Now we've got a pandemic and guess what, everybody is looking at this area of the market again with protection. And I think to your point, it's a great Russian. What we're finding is the word hybrid and it's it's a well overplayed term, but it's reality of the scenario. You know, we came through and started our journey of being here in the virtual world, but we moved into the physical and that's where we've been having so much success with HP as well. And now as we move towards that cloud world, um and to a degree, the application world with office 365 etcetera, what you're seeing is that hybrid need. We're seeing that the large enterprises that have relied on HP for so many years are also looking for that ubiquitous data protection layer. And because we have it so well baked into all the different parts of the portfolio, it's a seamless ability to just continue to exp fan utilization of the portfolio. So from our point of view, we're seeing fantastic against bright success. We're seeing it in some of these verticals like medical, like financial, the big corcoran pillars of society is related to the economic and industrial models. We're seeing those areas come on board, but we're also seeing people look at what I would classify as some of the Greenfield projects and that's a different viewpoint because if you look back at the history of HP as well, they were fantastic provider for the foundation of the core business. Now, what we're finding is that coming to HP envy and saying, hey, new areas Greenfield want to start fresh with a new approach, less of the legacy concern I've had before. How can we look at these new projects I'm working on. So we're seeing in the enterprise, we're seeing in what I would classify as traditional type of verticals and now we're starting to see that acceleration in some of these Greenfield projects, which is key. And that's something we've really, really enjoyed. And last part I'd say on that one as well is from a geographic basis. We are seeing all of our regions come up. Um, and the reason why that's important is sometimes you see alliances that have success in one market or one area, We're seeing the year over year growth in a mere be faster than we've ever seen. We're seeing are America's growth growth year over year and Asia is continuing to explode for us together. And so from that point of view, I think what that's telling us is that the customers resonate on what we're producing together. And so from that point of view, we're very ubiquitous in our level of value to customers and we're hoping to carry that on moving forward. >>Well, it's two trusted brands. Obviously, you know, the Hewlett Packard enterprise name and that stands out and is no longer start up with a funny name is >>you're proven >>In the marketplace, you just had a major release. I think it was V- 11. I'm not great the greatest products but um, earlier this year, wondering how that impacted the alliance? Was that fit? >>Yeah. Great question. And to your point, some people still have trouble with the name, but overall you're right, we do tend to find that we're in a good spot nowadays with regards to recognition and I D. C. Just released some fantastic statistics on growth and another record breaking year for being both from the sequential growth and the year over year growth for the second half of 2020. Moving us up into the number two position for the first time, which again, is a testament to the success were also having with HP and when you look at what happened on V 11, because as I mentioned at the start of this discussion, every one of our major releases has had HPV baked into it. And V 11 was a big release for us. There was a lot of pent up development work we were trying to get done and what we focused on with this again, especially for the enterprise, was looking at the HP portfolio and looking at faster speeds, faster speeds, have an economic value. We increased our speed and performance with HP primera, we increase it with HP nimble. We also made a really significant when we're working with HP store. Once we did a lot of evolution on that for a huge space savings, which together really values the customer and then finally where we've also found the customers asked for a lot of development from us together is consolidated with an all in one backup type of approach with the HP Apollo series. So from that point of view, we focused on the experience of the customer because the integrations are so solid. We're now fine tuning to increase that ri for the customer and V 11 was a big component of that, what I >>love about Wien David. So I used to be an I. D. C. For years and you just mentioned that the study that came out and you're number two and >>I've been talking a lot of your >>executives recently, you've, you've, you've thrown out that stand a lot number two. Number two. But, but when I was in to see everybody wanted to be number one at something, so you could say, oh, hey, we're number one backup company with the green logo. Hey, we're number one, >>but you're not >>doing that. And I'm joking about the green logo, but you actually are the number one. I think I'm correct in saying this, the number one pure play and back up in data protection and you don't, you don't stand up on that mantle. And I was asking some executives why? And you're like, well, no, because we want to be number one, that's what, that's our objective. You know, we're not gonna claim number one now until we get to number one and we'll claim real number one. So I like that about you guys. You, you set the mark the mark high. But so I love that. Um, >>I appreciate it. Yeah. How should >>people be thinking about the future of your relationship with HP the rest of this year and beyond? >>Yeah, great question. And I do really do appreciate that comment because it's an easy one to sort of pick up on it. And it comes down to the attitude. It comes down to our attitude with regards to there's nothing wrong with fight. There's nothing wrong with making sure you continue to have a north star that you never want to stop getting too. And I think that's a testament to the development of the products and, and overall our attitude to working in the field and working with our alliances. And when you look about, when you ask the question, excuse me. Dave about, you know, where do we see the HP envy moving forward, consistency, consistency is key for us for 10 years. We've been consistent in providing value And we want to continue doing that for another 10 years moving forward. And as we evolve our portfolio and you look at our Act two and as you talked about some of the things you talk to are the executives about. When you look at, we're moving forward, we're doing that in conjunction and we believe as you move forward with regards to some of the things HPR Do we want that consistency of integration? We want that consistency of experience to the customer. We want that consistency of listening and developing our engineering resources together to address that need. And again, it sounds like a really obvious answer and it is, but the difference on the back of this one, to be honest with you, Davis, we proved this again and again and again. And as you look at the Truman data protection solution and you do it in conjunction with HP, it's one of those things where we're so proud to make sure we keep working hard together and pushing each other to be better for our customers, that we're really excited about how it moves forwards. Were also, and again, we're not going into any juicy secrets here, but I wouldn't be surprised if V 12 that comes here in the, in the future also has another little nice street related to HPV as well. So from that point of view, um, you should have consistency, you should have trust and you should be excited about the fact that the investment and the joint alliance is stronger than it's ever been. >>Well, you guys are setting the marks, uh, certainly the competitive landscape gets tougher and tougher, but you guys are, are leading, you're moving fast, you get a great product to move at the speed, the speed you're, you are and growing at the pace you are for a billion dollar company is impressive. So congratulations on that and you're not done yet. So thanks >>for, thanks for that. We're excited about discover here. This is again, another, I think this is almost the ninth plus year. We've been been a strong sponsor of it. We're excited about H. P. S future as well here together. Um, and hey, we do this together. So we're great to see it moving forward, >>David, Great to see you again. Thanks so much. >>Thanks so much. Dave as always appreciate the time. >>Thank you for being with us. For HP. You discover 2021, the virtual edition. You're watching the Cube, the leader in digital tech coverage. >>Mm.

>>Welcome >>back to HP discover 2021. My name is Dave Volonte and you're watching the cubes virtual coverage of discover 21 we're excited to welcome back our wa Kadoura, She's the vice president and worldwide go to market leader for HP. Es smoking hot. Green Lake Cloud services are welcome back to the cube. Good to see you again. >>Thank you for having me to be with you. >>So talk about how your products and services are supporting customer transformations. I'm interested in the experience that everybody has been dreaming about describe how you're giving your customer that competitive advantage. If you've got any examples, that would be awesome. >>Yeah, you got it. Um, I think as we heard Antonio say the cloud is an experience, not a destination, right? And what we're doing with Green Lake is bringing those cloud capabilities and the cloud experience to our customers. You know, we like to say co locations, data center and edge of course. So this is the cloud on prem. And so rather than forcing customers to only have to go up to cloud to get modern cloud capabilities or the benefits of things like, you know, pay as you go for consumption, et cetera. You know, cloud native capabilities like containers, leveraging, kubernetes. We now bring all of that to Green Lake and to our customers. Edge locations and co locations and data centers. We've been able to dramatically transform many of our customers businesses. Right? And you'll probably see it discover some of those examples come to life. For example, Care Stream, who is, you know, in the electronic medical imaging world, Right? They have all of the X ray equipment that capture x rays and different sort of diagnostics for patients. Um and we worked with them to not only craft a ml solution to better read and diagnose these images, um but also all of the underlying infrastructure with the HP Green Lake Ml ops platform that allows them to instantly leverage the capabilities of machine learning and the infrastructure to go with it. >>And so tell me, so, how is it resonating with customers? What you're out there, talking to customers all the time? What are they >>telling you? You know, I think what our customers appreciate about HP Green Lake is it's not sort of look, it's either all on prem in my data center and I have to fully manage it, build it, implement it, take care of it or it's fully public cloud. I have little control and basically I get whatever the public cloud gives me right. Hp Green leg gives our customers the flexibility and control that they require. Right. And so you can think of many use cases where customers have a need to have the compute storage sort of processing need to happen closer to where their data and apps live. Um and so for that exact reason our customers love the flexibility, right? Cloud one, Dato was public cloud. Cloud to Dato I think is the cloud that comes to our customers at their convenience. And to me, you know what I tell C I O S and C T O S and sort of other lines of business leaders when I meet with them is you shouldn't be forced to have to take your data and apps elsewhere to get the transformation that you need. We want to be able to bring that directly to our customers. >>Of course, a lot of the transformation is around data. We love talking about data on the cube and it's funny, I mean we talked about big data last decade. We don't use that term much anymore. Uh It was kind of overhyped but as as often times as the case may be in the early days it's overhyped but then it's under hyped when it actually starts to kick in. And I feel like we're entering a new age of data. And insights with the ascendancy of machine learning and ai what does this mean from H. P. S. Perspective and what our customers telling you that it means for them. >>Yeah no data. I think we often hear data is the new currency writes the new gold. Um You know we've heard uh Antonio even say things like look data could even become something that maybe over time companies start to put some kind of value on their balance sheet. Behind right the same way that maybe brands represented this value on a balance sheet. Um effectively what's happened with data is a lot of people have a lot of data but there's not been a lot of ability to extract insights from data. Right? And I think this is the new revolution that we're all undergoing is we finally have the modern analytics tools to actually turn the data into insights and what we bring to the table from an HPD perspective is the fact that we have the best infrastructure. We obviously now have the cloud capabilities mixed in with our data fabric or container platform, our machine learning operations platform to then be able to process that data again, integrated with many of the great I SV partners that we have on the data side allow our customers to turn that into real insights for their business. And effectively data is becoming a huge competitive advantage. Right? I think many of us are you know, leveraging some, you know, pretty interesting tools or gadgets these days, right? Like I wear one of those, you know, sleep brings, you can imagine a company like that in the future that's able to collect so much data from the folks that purchase their products. Then being able to give us insights about, you know, where is the best zip code that, you know, people get the most amount of sleep in or you know, which zip codes are the healthiest and you know, the United States or countries, et cetera. Um but data really is becoming um you know, a competitive advantage. And one of the things that we care most about at HP is also using it as a force for good and making sure that there is a sort of ethical ai capability. >>That's a great message and very important one. And and it's interesting you're saying about data and the value how well it's clearly it's clearly being valued in terms of companies market caps. I mean it's it's it's you know, maybe it's not the balance yet, but it's on the income statement in terms of data products and data services that that's happening. So we'll see if Antonio's right in the next, you know, several years. But so let's talk more about the specific data challenges that you're solving for your customers. They talk about silos, they talk about they haven't got as much value out of their data initiatives as they wanted to. What are they telling you? Are there challenges and how are you approaching it? >>Yeah, I think, you know, um data's everywhere right. The ability for customers to store the right amount of data is a huge challenge because obviously there's, you know, a huge cost associated with collecting, keeping cleansing processing, you know, all the way to sort of analyzing your data. There tends to be a ton of data silos, right? So customers are looking for a common data fabric that they can then process their data sources across and then be able to sort of tap into that data from an analytics perspective. So much of the technology again that we're focused on is be able to store the data right, Our data fabric layer with his moral right being able to process that data capture that data and then allow the analytics tools to then harness the power of that data and turn that into real business insights for our customers. Um Every customer that I have spoken to, you know whether their financial services, you know, you can imagine the big financial services. I mean they've got you know, just bazillions of pockets of data everywhere and you know, the real sort of a challenge for them is how do I build a common data platform that allows me to tap into that data in effective ways for my business users? >>You talk a little bit about how you're changing the way you're providing solutions? Maybe maybe you could contrast it with the way HP has done in the past, because I think that's important when you, when you think about, you talk a lot about green lake and as a service, but if the products are still, you know, kind of boxes and Luns and, and gigahertz and ports, then, you know, that's that's a dis continuity. So what's changed from the past and how are you feeding into the way customers are transforming their business and supporting their outcomes? >>That's exactly right. You know, at some point in time, right. If you think maybe 10 or 20 years back, it used to be very much about the infrastructure for hp. What's exciting about what we're doing differently for our customers is look, we have the best infrastructure in the business, right. Hp has been doing this you no longer than anyone has probably almost 60 years now. Um but being able to vertically integrate right, move up in that value change so that our customers can get more complete solutions is the more interesting part for our customers. Our customers love our technology, Yes, the gigahertz and the speeds and feeds all of that do matter because they, you know, make for some very powerful infrastructure. However, what makes it easier is the fact that we are building platform stacks on top of that hardware, um that help abstract away the complexity of that infrastructure and the ability to use it far more seamlessly. Um and then if you think about it, we of course have also one of the most advanced services organizations. So being able to leverage our services capabilities, our platform capabilities on top of that hardware, again, deliver it back to our customers In a consumption model, which they've become two X, which they've come to expect from a cloud model. Um and then surrounded by a very rich ecosystem of partners. And we're talking about system Integrators that now have capabilities on helping our customers run their Green Lake environments. We're talking about I. S. V. S. Right? So software stacks and platforms that fully integrate with the Green Lake platform for completely seamless solutions. Um as well as channel Partners and global distributors. So I think that's where we can truly deliver the ultimate end to end solution. It's not just the hardware, right? But it's being complemented with the right services being complemented with the right platform capabilities, the software integrations to deliver that workload that the customer expects. >>And partners, they gotta they gotta place bets, they gonna put resources time money in a line, their resources with with their their partners and their suppliers like HP. So when they ask you, hey, okay. Hp. Tell me or well, what's your overall strategy? Why is it compelling and why do you give me competitive advantage relative to some of your peers in the industry? >>Yeah, I think what, you know, partners are going to be most excited about is the openness of the platform, right? Being able to allow our partners to leverage Green Lake Central with open API so that they can integrate some of their own technologies into our platform. Uh the ability to allow them to also layer in their own um managed services on top of the platform is key. Um And of course being able to build sort of these win win solutions with the system Integrators, right. The system Integrators have some fantastic capabilities all the way from an application development, all the way down to the infrastructure management and data center delivery centers that they have. And so leveraging HP Green Lake um really helps them have access to core technologies that they need to deliver these solutions. >>I wonder if I could take a little sort of side road here and ask you because so many changes going on HP itself is transforming your customers are transforming the pandemic has accelerated all these transformations. Can you talk a little bit about how you've transformed go to market specifically in the context of of as a service? I mean that had to be quite a change for you guys. >>Yeah, no, go to market transformations in support of sort of moving from traditional go to markets right to call, go to markets or significant. Um They required us to really think through what does delivering as a service solutions mean for our direct sales force? What does it mean for our partners and their transformations and being able to support as a service solutions? Um for HP specifically, it also means um thinking about our customer outcomes, not just our ability to ship them, you know, the requisite hardware and say, look, once it's left our dog, our job is done right. It really takes our obligation all the way to the customer using the technology on a day by day basis, as well as supporting them in making sure that everything from implementation to set up to the ongoing monitoring operations of the technology is working for them in the way that they expect in an as a service way, right? We don't expect them to operate it. We don't expect them to, you know, do anything more than pick up the phone and call us if something doesn't go as planned. >>And how about your sellers and your partners? How did they respond? I mean you just wake up one day is okay guys, here we go, new compensation scheme, new way to sell new way to market that that took some thought in some time. And where are you in that journey? >>That's right. And I always say, you know, if you expect people to wake up one day and be transformed, right? You're kidding yourself. Um So everything from sort of the way that we think about our customers use cases, right? And empowering our sellers to understand the outcomes that our customers expect and demand from us to things like compensation too. You know, the partner rebate program that we leverage through the channel partners in order to give them the right incentives to also allow them to make the right investments to support Green Lake. Um, you know, we've all, you know, HP has a fairly significant field sales and solution team and so not thinking about this only as a single person that represents Green Lake, but looking at our capabilities across the board, right. We have fantastic advisory consultants on the ground with phds and data science. We have folks that understand, you know, high performance computing, so making sure that we're embedding the expertise in all of the right personas that support our customers, not just from a calm perspective, but also from an understanding of the end to end solutions that we're bringing to those markets. >>So what gets you stoked in the morning, you get out of bed and you're like, okay, I'm gonna go attack the world. What are you most excited about for H P E and his future? >>You know, it's, there's so much happening right now in this sort of cloud world. Right? Um to me the most exciting portion is the fact that given that we've now introduced on prem cloud to the world, our ability to ship new services and new capabilities, um, but also do that via a very rich partner ecosystem honestly is what probably has me most excited. This is no longer the age of go at it alone, Right. So not only are engineering and product teams hard at work in the engine room producing, you know, capabilities at sort of lightening fast speeds, but it's also our ability to partner, uh you know, whether it's with platform providers, you know, software providers or, you know, system Integrators and services providers, that ecosystem is starting to come together to deliver highly meaningful solutions to our customers and all in a very open way. Um, the number one thing that I personally care about is that our customers never feel like they are being locked in or that they are sort of being forced to have to give up certain levels of capabilities. We want to give them the best of what's out there and allow them to then have that flexibility in their solution. >>And one of the challenges, of course with virtual events is you don't have the hallway track. You know, somebody can't say, hey, have you seen that IOT zone? It's amazing. They got all these robots going around. But so what, what would you say that people should be focused on the discovery maybe things that you want to call out specific highlights or segments that you think are relevant? >>Yeah, there's gonna be a ton of fantastic stuff I think, um you know, really looking for that edge to cloud strategy, um that we're gonna be spending a lot of time talking about um looking at some of our vertical workload solutions, right. We're gonna be talking about quite a few from electronic health care records to payment solutions. Um and many more I think depending on what folks are interested in, there's gonna be something for everyone. Um Project Aurora, which now starts to announce our new security capabilities. Um, you know, the zero trust capabilities that we're delivering um is probably interesting to a lot of our customers, so lots of exciting things coming and I'm excited for our customers to check this out. >>No doubt that's a hot topic. Especially given what's been happening the news these past several months. All right, well, thanks so much for coming back in. The cube is great to see you hopefully face to face next time. >>I sure hope so. Thanks so much for having me. >>It's our pleasure. And thank you for watching and thank you for being with us and our ongoing coverage of HPD discovered 2021. This is Dave Volonte. You're watching the cube, The leader and digital tech coverage. >>Yeah.

>>Welcome back to HPD discovered 2021 the cubes virtual coverage, continuous coverage of H P. S H. P. S. Annual customer event. My name is Dave Volonte and we're going to dive into the intersection of high performance computing data and AI with DR Eng limb go who is the senior vice president and CTO for AI at Hewlett Packard enterprise Doctor go great to see you again. Welcome back to the cube. >>Hello Dave, Great to talk to you again. >>You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the day two keynotes here at discover you talked about thriving in the age of insights and how to craft a data centric strategy and you addressed you know some of the biggest problems I think organizations face with data that's You got a data is plentiful but insights they're harder to come by. And you really dug into some great examples in retail banking and medicine and health care and media. But stepping back a little bit with zoom out on discovered 21, what do you make of the events so far? And some of your big takeaways? >>Mm Well you started with the insightful question, right? Yeah. Data is everywhere then. But we like the insight. Right? That's also part of the reason why that's the main reason why you know Antonio on day one focused and talked about that. The fact that we are now in the age of insight. Right? Uh and and uh and and how to thrive thrive in that in this new age. What I then did on the day to kino following Antonio is to talk about the challenges that we need to overcome in order in order to thrive in this new age. >>So maybe we could talk a little bit about some of the things that you took away in terms I'm specifically interested in some of the barriers to achieving insights when you know customers are drowning in data. What do you hear from customers? What we take away from some of the ones you talked about today? >>Oh, very pertinent question. Dave you know the two challenges I spoke about right now that we need to overcome in order to thrive in this new age. The first one is is the current challenge and that current challenge is uh you know stated is you know, barriers to insight, you know when we are awash with data. So that's a statement right? How to overcome those barriers. What are the barriers of these two insight when we are awash in data? Um I in the data keynote I spoke about three main things. Three main areas that received from customers. The first one, the first barrier is in many with many of our customers. A data is siloed. All right. You know, like in a big corporation you've got data siloed by sales, finance, engineering, manufacturing, and so on, uh supply chain and so on. And uh, there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the that was the first barrier we spoke about barriers to incite when we are washed with data. The second barrier is uh, that we see amongst our customers is that uh data is raw and dispersed when they are stored and and uh and you know, it's tough to get tough to to get value out of them. Right? And I in that case I I used the example of uh you know the May 6 2010 event where the stock market dropped a trillion dollars in in tens of ministerial. We we all know those who are financially attuned with know about this uh incident But this is not the only incident. There are many of them out there and for for that particular May six event uh you know, it took a long time to get insight months. Yeah before we for months we had no insight as to what happened, why it happened, right. Um and and there were many other incidences like this. And the regulators were looking for that one rule that could, that could mitigate many of these incidences. Um one of our customers decided to take the hard road go with the tough data right? Because data is rolling dispersed. So they went into all the different feeds of financial transaction information. Uh took the took the tough uh took the tough road and analyze that data took a long time to assemble and they discovered that there was court stuffing right? That uh people were sending a lot of traits in and then cancelling them almost immediately. You have to manipulate the market. Um And why why why didn't we see it immediately? Well the reason is the process reports that everybody sees uh rule in there that says all trades. Less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 103 100 100 shares trades uh to fly under the radar to do this manipulation. So here is here the second barrier right? Data could be raw and dispersed. Um Sometimes you just have to take the hard road and um and to get insight And this is 1 1 great example. And then the last barrier is uh is has to do with sometimes when you start a project to to get insight to get uh to get answers and insight. You you realize that all the datas around you but you don't you don't seem to find the right ones To get what you need. You don't you don't seem to get the right ones. Yeah. Um here we have three quick examples of customers. 111 was it was a great example right? Where uh they were trying to build a language translator, a machine language translator between two languages. Right? But not do that. They need to get hundreds of millions of word pairs, you know, of one language compared uh with the corresponding other hundreds of millions of them. They say we are going to get all these word pairs. Someone creative thought of a willing source and a huge, so it was a United Nations you see. So sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data right. The second one has to do with uh there was uh the uh sometimes you you may just have to generate that data, interesting one. We had an autonomous car customer that collects all these data from their cars, right, massive amounts of data, loss of senses, collect loss of data. And uh you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car uh in in um in fine weather and collected the car driving on this highway in rain and also in stone, but never had the opportunity to collect the car in hale because that's a rare occurrence. So instead of waiting for a time where the car can dr inhale, they build a simulation you by having the car collector in snow and simulated him. So these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated the fact that data is silo Federated, it various associated with data. That's tough to get that. They just took the hard road, right? And sometimes, thirdly, you just have to be creative to get the right data you need, >>wow, I tell you, I have about 100 questions based on what you just said. Uh, there's a great example, the flash crash. In fact, Michael Lewis wrote about this in his book, The Flash Boys and essentially right. It was high frequency traders trying to front run the market and sending in small block trades trying to get on the front end it. So that's and they, and they chalked it up to a glitch like you said, for months, nobody really knew what it was. So technology got us into this problem. I guess my question is, can technology help us get out of the problem? And that maybe is where AI fits in. >>Yes, yes. Uh, in fact, a lot of analytics, we went in, uh, to go back to the raw data that is highly dispersed from different sources, right, assemble them to see if you can find a material trend, right? You can see lots of trends right? Like, uh, you know, we, if if humans look at things right, we tend to see patterns in clouds, right? So sometimes you need to apply statistical analysis, um math to be sure that what the model is seeing is is real. Right? And and that required work. That's one area. The second area is uh you know, when um uh there are times when you you just need to to go through that uh that tough approach to to find the answer. Now, the issue comes to mind now is is that humans put in the rules to decide what goes into a report that everybody sees in this case uh before the change in the rules. Right? But by the way, after the discovery, the authorities change the rules and all all shares, all traits of different any sizes. It has to be reported. No. Yeah. Right. But the rule was applied uh you know, to say earlier that shares under 100 trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and and under for understandable reasons. I mean they probably didn't want that for various reasons not to put everything in there so that people could still read it uh in a reasonable amount of time. But uh we need to understand that rules were being put in by humans for the reports we read. And as such, there are times you just need to go back to the raw data. >>I want to ask, >>albeit that it's gonna be tough. >>Yeah. So I want to ask a question about AI is obviously it's in your title and it's something you know a lot about but and I want to make a statement, you tell me if it's on point or off point. So it seems that most of the Ai going on in the enterprise is modeling data science applied to troves of data >>but >>but there's also a lot of ai going on in consumer whether it's you know, fingerprint technology or facial recognition or natural language processing. Will a two part question will the consumer market has so often in the enterprise sort of inform us uh the first part and then will there be a shift from sort of modeling if you will to more you mentioned autonomous vehicles more ai influencing in real time. Especially with the edge. She can help us understand that better. >>Yeah, it's a great question. Right. Uh there are three stages to just simplify, I mean, you know, it's probably more sophisticated than that but let's simplify three stages. All right. To to building an Ai system that ultimately can predict, make a prediction right or to to assist you in decision making, have an outcome. So you start with the data massive amounts data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data and the machine uh starts to evolve a model based on all the data is seeing. It starts to evolve right to the point that using a test set of data that you have separately campus site that you know the answer for. Then you test the model uh you know after you trained it with all that data to see whether it's prediction accuracy is high enough and once you are satisfied with it, you you then deploy the model to make the decision and that's the influence. Right? So a lot of times depend on what what we are focusing on. We we um in data science are we working hard on assembling the right data to feed the machine with, That's the data preparation organization work. And then after which you build your models, you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you you pick one model and the prediction isn't that robust, it is good but then it is not consistent right now what you do is uh you try another model so sometimes it's just keep trying different models until you get the right kind. Yeah, that gives you a good robust decision making and prediction after which It is tested well Q eight. You would then take that model and deploy it at the edge. Yeah. And then at the edges is essentially just looking at new data, applying it to the model, you're you're trained and then that model will give you a prediction decision. Right? So uh it is these three stages. Yeah, but more and more uh you know, your question reminds me that more and more people are thinking as the edge become more and more powerful. Can you also do learning at the edge? Right. That's the reason why we spoke about swarm learning the last time, learning at the edge as a swamp, right? Because maybe individually they may not have enough power to do so. But as a swampy me, >>is that learning from the edge or learning at the edge? In other words? Yes. Yeah. Question Yeah. >>That's a great question. That's a great question. Right? So uh the quick answer is learning at the edge, right? Uh and also from the edge, but the main goal, right? The goal is to learn at the edge so that you don't have to move the data that the Edge sees first back to the cloud or the core to do the learning because that would be the reason. One of the main reasons why you want to learn at the edge, right? Uh So so that you don't need to have to send all that data back and assemble it back from all the different edge devices, assemble it back to the cloud side to to do the learning right? With swampland. You can learn it and keep the data at the edge and learn at that point. >>And then maybe only selectively send the autonomous vehicle example you gave us. Great because maybe there, you know, there may be only persisting, they're not persisting data that is inclement weather or when a deer runs across the front and then maybe they they do that and then they send that smaller data set back and maybe that's where it's modelling done. But the rest can be done at the edges. It's a new world that's coming down. Let me ask you a question, is there a limit to what data should be collected and how it should be collected? >>That's a great question again. You know uh wow today, full of these uh insightful questions that actually touches on the second challenge. Right? How do we uh in order to thrive in this new age of inside? The second challenge is are you know the is our future challenge, right? What do we do for our future? And and in there is uh the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that I talk about what to collect right? When to organize it when you collect and then where will your data be, you know going forward that you are collecting from? So what, when and where for the what data for the what data to collect? That? That was the question you ask. Um it's it's a question that different industries have to ask themselves because it will vary, right? Um let me give you the you use the autonomous car example, let me use that. And you have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from the fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars collecting data so they can train and eventually deploy commercial cars, right? Um so this data collection cars they collect as a fleet of them collect temporal bikes a day. And when it came to us building a storage system to store all of that data, they realized they don't want to afford to store all of it. Now, here comes the dilemma, right? What should I after I spent so much effort building all these cars and sensors and collecting data, I've now decide what to delete. That's a dilemma right now in working with them on this process of trimming down what they collected. You know, I'm constantly reminded of the sixties and seventies, right? To remind myself 60 and seventies, we call a large part of our D. N. A junk DNA. Today. We realize that a large part of that what we call john has function as valuable function. They are not jeans, but they regulate the function of jeans, you know, So, so what's jump in the yesterday could be valuable today or what's junk today could be valuable tomorrow, Right? So, so there's this tension going on right between you decided not wanting to afford to store everything that you can get your hands on. But on the other hand, you you know, you worry you you you ignore the wrong ones, right? You can see this tension in our customers, right? And it depends on industry here, right? In health care, they say I have no choice. I I want it. All right. One very insightful point brought up by one health care provider that really touched me was, you know, we are not we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But you also care for the people were not caring for. How do we find them? Mhm. Right. And that therefore, they did not just need to collect data. That is that they have with from their patients. They also need to reach out right to outside data so that they can figure out who they are not caring for, right? So they want it all. So I tell us them, so what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and find that. Of course they also come back to us rightfully that you know, we have to then work out a way to help them build that system, you know? So that's health care, right? And and if you go to other industries like banking, they say they can't afford to keep them off, but they are regulated, seems like healthcare, they are regulated as to uh privacy and such. Like so many examples different industries having different needs, but different approaches to how what they collect. But there is this constant tension between um you perhaps deciding not wanting to fund all of that uh all that you can store, right? But on the other hand, you know, if you if you kind of don't want to afford it and decide not to store some uh if he does some become highly valuable in the future, right? Yeah. >>We can make some assumptions about the future, can't we? I mean, we know there's gonna be a lot more data than than we've ever seen before. We know that we know well notwithstanding supply constraints on things like nand. We know the prices of storage is going to continue to decline. We also know, and not a lot of people are really talking about this but the processing power but he says moore's law is dead okay. It's waning. But the processing power when you combine the Cpus and NP US and GPUS and accelerators and and so forth actually is is increasing. And so when you think about these use cases at the edge, you're going to have much more processing power, you're gonna have cheaper storage and it's going to be less expensive processing And so as an ai practitioner, what can you do with that? >>Yeah, it's highly again, another insightful questions that we touched on our keynote and that that goes up to the why I do the where? Right, When will your data be? Right. We have one estimate that says that by next year there will be 55 billion connected devices out there. Right. 55 billion. Right. What's the population of the world? Of the other? Of 10 billion? But this thing is 55 billion. Right? Uh and many of them, most of them can collect data. So what do you what do you do? Right. Um So the amount of data that's gonna come in, it's gonna weigh exceed right? Our drop in storage costs are increasing computer power. Right? So what's the answer? Right. So, so the the answer must be knowing that we don't and and even the drop in price and increase in bandwidth, it will overwhelm the increased five G will overwhelm five G. Right? Given amount 55 billion of them collecting. Right? So, the answer must be that there might need to be a balance between you needing to bring all that data from the 55 billion devices of data back to a central as a bunch of central Cause because you may not be able to afford to do that firstly band with even with five G. M and and SD when you'll still be too expensive given the number of devices out there. Were you given storage cause dropping will still be too expensive to try and store them all. So the answer must be to start at least to mitigate the problem to some leave both a lot of the data out there. Right? And only send back the pertinent ones as you said before. But then if you did that, then how are we gonna do machine learning at the core and the cloud side? If you don't have all the data you want rich data to train with. Right? Some sometimes you want a mix of the uh positive type data and the negative type data so you can train the machine in a more balanced way. So the answer must be eventually right. As we move forward with these huge number of devices out of the edge to do machine learning at the edge. Today, we don't have enough power. Right? The edge typically is characterized by a lower uh, energy capability and therefore lower compute power. But soon, you know, even with lower energy, they can do more with compute power improving in energy efficiency, Right? Uh, so learning at the edge today, we do influence at the edge. So we data model deploy and you do influence at the age, that's what we do today. But more and more, I believe, given a massive amount of data at the edge, you you have to have to start doing machine learning at the edge. And and if when you don't have enough power, then you aggregate multiple devices, compute power into a swamp and learn as a swan, >>interesting. So now, of course, if I were sitting and fly on the wall in HP board meeting, I said, okay, HP is as a leading provider of compute, how do you take advantage of that? I mean, we're going, I know it's future, but you must be thinking about that and participating in those markets. I know today you are you have, you know, edge line and other products. But there's it seems to me that it's it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that >>opportunity for your customers? Uh the world will have to have a balance right? Where today the default, Well, the more common mode is to collect the data from the edge and train at uh at some centralized location or a number of centralized location um going forward. Given the proliferation of the edge devices, we'll need a balance. We need both. We need capability at the cloud side. Right. And it has to be hybrid. And then we need capability on the edge side. Yeah. That they want to build systems that that on one hand, uh is uh edge adapted, right? Meaning the environmentally adapted because the edge different they are on a lot of times on the outside. Uh They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery powered. Right? Um so you have to build systems that adapt to it, but at the same time they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run rich a set of applications. So yes. Um that's that's also the insightful for that Antonio announced in 2018, Uh the next four years from 2018, right, $4 billion dollars invested to strengthen our edge portfolio, edge product lines, right Edge solutions. >>I get a doctor go. I could go on for hours with you. You're you're just such a great guest. Let's close what are you most excited about in the future of of of it? Certainly H. P. E. But the industry in general. >>Yeah I think the excitement is uh the customers right? The diversity of customers and and the diversity in a way they have approached their different problems with data strategy. So the excitement is around data strategy right? Just like you know uh you know the the statement made was was so was profound. Right? Um And Antonio said we are in the age of insight powered by data. That's the first line right? The line that comes after that is as such were becoming more and more data centric with data the currency. Now the next step is even more profound. That is um you know we are going as far as saying that you know um data should not be treated as cost anymore. No right. But instead as an investment in a new asset class called data with value on our balance sheet, this is a this is a step change right in thinking that is going to change the way we look at data the way we value it. So that's a statement that this is the exciting thing because because for for me a city of AI right uh machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. So so that's that's why when when people start to value data right? And and and say that it is an investment when we collect it. It is very positive for ai because an Ai system gets intelligent, more intelligence because it has a huge amounts of data and the diversity of data. So it'd be great if the community values values data. Well >>you certainly see it in the valuations of many companies these days. Um and I think increasingly you see it on the income statement, you know data products and people monetizing data services and maybe eventually you'll see it in the in the balance. You know Doug Laney when he was a gardener group wrote a book about this and a lot of people are thinking about it. That's a big change isn't it? Dr >>yeah. Question is is the process and methods evaluation. Right. But uh I believe we'll get there, we need to get started then we'll get their belief >>doctor goes on and >>pleasure. And yeah and then the yeah I will will will will benefit greatly from it. >>Oh yeah, no doubt people will better understand how to align you know, some of these technology investments, Doctor goes great to see you again. Thanks so much for coming back in the cube. It's been a real pleasure. >>Yes. A system. It's only as smart as the data you feed it with. >>Excellent. We'll leave it there. Thank you for spending some time with us and keep it right there for more great interviews from HP discover 21. This is dave a lot for the cube. The leader in enterprise tech coverage right back.

>>from the cube studios in Palo alto in boston connecting with thought leaders all around the world. This is a cute conversation. >>Welcome to the cubes coverage of HP discoverer 2021. I'm lisa martin Janice Zenga's joins me next. The vice president of innovation for social impact in H. P. S. Office of the C T. O Janice. Welcome to the cube. Hi lisa. Great to be here. So let's talk about this. You lead H. P. S. Tech for good program. I always love talking about programs like this. Talk to me about that industry tech academia government partnering to solve key challenges that society is facing and crack that for us. Yeah. So so >>we we um are really proud to be able to look at big challenges in the world and look where our strengths, where our innovations are emerging technologies and our employee expertise could actually contribute to a problem. And so >>we began >>a program uh to actually pick some projects particularly in food systems, world hunger and Health Systems, where we thought some of our technologies could really be impactful. And so >>we have been working with a number of >>clients and partners to actually uh work on ai contributions, high performance compute contributions um and uh and a contribution around this notion of data spaces that we're talking about all of these emerged through um complex interactions around social, good engagement. >>So the concept you mentioned, data space is the concept of data spaces isn't new but do explain that. Give us an overview Janice for those folks that might not be familiar with what it is. >>So so the notion of data spaces is to connect data producers to data consumers. And so um in the past um you know connecting producers and consumers has really been limited about, you know, where is your data located? Um Do you have access to the right data? Um Is the data a good quality set of data? What's the providence of that data? What's the quality of it? And is it trustworthy? And so um >>our >>concept of data spaces is actually trying to address all of those um notions with with a new approach >>so collecting ensuring data isn't anything new. But of course what we talk about every day on this program is the volume of data in that context. What are some of the challenges that you're seeing with clients and how can you help them eliminate those challenges and be able to make data driven decisions? >>So um the first challenge is finding the data. And uh there is a big challenge. I mean there's new roles emerging called data hunters and a great amount of time being spent by data scientists just trying to find sources of data. And that's a big challenge. And then when you find this data, is it in the right format and how expensive is it to move the data so that you could have it in a place where it can actually be analyzed. So, um, so what we're working on recognizing that there is a vast amount of data at the edge, a vast amount of data that's probably never going to move from the edge and from those locations. Um but what we're trying to do is recognize that and actually work to bring the algorithms and the analytics to the data and to work with making sure that data is accessible >>and can be >>understood and >>and processed uh in a consistent way. And >>today there is um a lot >>of silos in in place around uh where data >>exists. And uh and so our approach here is to kind of address is from an open source community perspective to build uh and and provide >>a metadata layer, >>standard of all standards. Kind of a super metadata layer for a non technical way to represent that. And and then to use that to help um connect to uh analytics platforms, both citizen users. Um, you know, subject matter experts who may not be data scientists as well as the data scientists. So actually being able to connect a broader set of users into data analytics that are currently available and have the knowledge to be able to get information and insights out of that data. >>So democratizing that access to data. One of the things I'm curious about what you've seen is that's a cultural shift. You talked about some of the new rules. Data hunters and people get very sort of territorial about that. How I'm just curious what are some of the things that you've seen that where HP and data spaces have been able to help companies to be able to democratize that access and also kind of transform their culture >>Well. So um a few >>things. First of all, there has >>to be a strong motivation >>for someone to share data and in order for them to feel safe and sharing that data. Um you know there has to be security and trust established and most data producers want to control who gets to see their data and under what conditions there needs to be governance of data as well. So those are important aspects that have to be in place. Our approach is to kind of build in exchange for that so that um data consumers understand the conditions in which they can access and use the data um And and also potentially contribute back the new datasets that they're creating through their analytics back into a catalogue being a provisioning of data. This improving kind of the standardization and the simplicity Of how data gets exchanged today. In effect allows a greater democratization of access of data so so that you don't have to be a data scientist. I mean data scientist today can spend 7-8 months actually getting their data that they're going to use um into a format that they can they can actually process. And we think that that's inefficient. We think there's a lot that can be done. Um The other challenge around this is that oftentimes data is multi entity. Even inside of a company, you can find data, you >>know, in different departments and different >>businesses. Um >>But even when you think beyond a >>company, if you think about entities that are that are, you know globally >>distributed um >>and maybe multi, you know, multi entity, there are new challenges about how data can come together from those sources and still be of the right providence and be understood to be trustworthy. >>Well, one of the things that I think one of the many things I think we've learned during the last year is that the, the need and access for real time data has been a critical factor in helping businesses pivot and survive versus those that that might not. So what are you seeing in terms of like you said, data scientist spending so much time getting access to clean data, the opportunities to miss, you know, opportunities for new products and services and to and to meet customer demand in new ways to talk to me about how data spaces can facilitate that faster real time access. >>Right? So, so by having an exchange that can be implemented inside of an enterprise or across enterprises, we actually think it allows some of that kind of pre work to be done, allows that cataloging and provisions. So you can come to uh come to a place, it's a place where an exchange can occur and actually be able to, you know, um get more ready >>access to the data. You don't have to >>necessarily go through a cleansing process and through a deep investigation on providence and then, you know, oftentimes uh you learn as you process data about new data or the data sets change. Right? So, so can there be improvements around keeping those ml algorithms current and helping you that in a very efficient way without having to rerun and rewrite code and rerun your algorithms um every single time. So we think there's a lot of improvement that can be done there as well. >>So let's look at, did a great job of explaining data space is the opportunity, the challenges that we've seen the opportunities. But let us help the audience understand what makes what HPV is doing with data spaces different, unique. What are some of the differentiators? There? >>A few things. One is we're approaching this from an open source approach, so we expect to be able to contribute back to the open source communities and allow for a greater ecosystem to develop around these solutions and that will enable greater sharing and trustworthy sharing. The second thing is security, we intend to apply a great security layer into this that allows data to be trusted um and then the governance capabilities, so being able to use things like our data fabric to actually help support um the governance that producers and consumers want to have uh is also important. And then finally being able to work multi cloud across um, on prem and in the cloud >>is a great >>advantage. So you don't get vendor lock in, you'll be able to be able to kind of minimize your data egress because >>maybe you're not gonna be doing data egress out of the cloud >>and instead you'll be you'll be able to process your data right where it's at without having to pay for that movement. >>And I imagine that would facilitate that speed of real time that I mentioned a minute ago. >>That's right, That's right. >>So let's now look at HP data spaces compared to data marketplace. Give me the compare and contrast with respect to those two. So, data >>marketplaces are typically very siloed and very specific to a sector or an industry today, um, and they're they're typically built on their own platforms and to end, they're not always open by design. Um so uh we expect to be able to support multiple data data marketplaces through a plug in into the data spaces um platform that that we build and that will allow greater connectivity and greater access to many different marketplaces. Um and so the data spaces is not intended to be siloed by industry or narrowly kind of focused, >>so helping to remove those silos, which we also, another thing that we talk about, what are some, I'm just curious some of the feedback from the open source community about what you're doing here building on this open foundation. >>So it's um it's actually been very positive. So the very first thing we did was because of our work as I start at the top of the conversation in agriculture, which is a great, a great example of where there's immense amounts of data that is not well standardized, are structured in a way that can be used towards addressing things like world hunger and some of the food supply and food system challenges. We have we uh in working through this >>kind of distilled some >>of the problems that to being this lack of access to data. And so one of the reasons we explored was like why is there this lack of use of data and lack of access to data? And it came down to not being able to access the data where it's generated and not being able to actually share it broadly across entities. And so, um so what we did is we joined the Linux Foundation has a new open source community called Ag stack and we are a founding company uh as part of that new community and we have shared the concepts around data spaces and the metadata layer standardisation that we've envisioned uh into the community and that's just getting kicked off. But it's also a great first step for us um to kind of build an open source community around it. >>Excellent. Sounds like you said positive feedback. If we crack open the hood of data spaces, what are some of the technologies that we see underneath that are making it and its evolution possible? >>Right? So um multi cloud uh across uh data, you know, support um edge processing um data fabric um Israel's solution as well, so being able to kind of move data and then of course, kind of a key layer. This is this notion of a metadata layer, standard on top >>of metadata layer standards. >>And what is that going to allow in terms of connecting the data consumers with the data producers? >>It's going to make it easier, it's going to make it faster, it's going to minimize costs. Uh it's gonna allow for a quality exchange with more information for consumers to have that trust and most importantly the security. Um and it will also create kind of motivation, kind of give and take because exchange has to be equitable for producers and consumers to both be at the >>table. That's a great point about about the being equitable. So this whole initiative that we've been talking about is coming out of the Office of the CTO at HP where we talked about. So the focus is on Uh projects that are emerging not yet on the road map. So what can we expect, what can your audience expect in the next 12-18 months? >>So our approach in the Office of the CTO is to take emerging technologies and ideas and actually bring them into kind of what we would call advanced development stages. So we do proof of concepts, we do a lot of piloting, we worked with customers and clients directly to kind of tune and test commercialization possibilities uh and value of a solution that we're evolving and to kind of get it ready for market if it makes sense to do that. And so We have proof of concepts with the dozens of customers right now in this topic area and more that want to join in and get involved in having access to it as well. Um so I would say most of the work we do in the coming 12 months will be driven by what these proof of concepts with these clients actually uncovered for us. Um and so we know first and foremost we're working with, you know, a large financial services company, we're looking we're working on the agricultural front with a number of important customers that are testing kind of a multi entity data sharing aspects. Were working also with the health care industry client, which is looking at extreme sets of large data that are kind of unanticipated datasets, you would normally think that would be important for disease prediction. And so all of those different kind of use cases are helping us kind of think about um, you know, which features are most important and by when I can tell you the security, the trustworthiness, the data provenance, the data governance are essential elements that are going to have to be there. >>I think those are essential elements that in any industry, especially that security front. >>Yes, very much so. >>So. In terms of the event at hp, what are some of the things that the audience is going to be able to to learn and glean about? Data sources, data spaces? >>So we've had a kind of a great three days um first starting out with Antonio neary and and and F. I. S to talk about kind of the the insight, the age of insights and and how data is actually becoming the currency of the future if you will. And so that we started that way. And then on day two we had a panel of some of our clients talking about in their particular industry, what's happening with data. So you start to see the kind of um sharing out of uh requirements and how urgent these requirements are growing. Uh And then on day three we actually go into more technology. So you'll see there. We have a number of demos and sessions Uh one specifically around agriculture use case, another around health care use case as well. And then we go into a little bit more detail around the data spaces concept in the keynote for day three. >>So action packed three days Janice. Thank you so much for joining me. Talking to us about data space is what you guys are doing for social impact out of h p. S. Office of the C t. O. We appreciate your time. >>Thank you lisa >>for Janice. Thank yous. I'm lisa martin. You're watching the cubes coverage of HP discover 2021 mm.

>>Welcome to the cubes coverage of HP discover 2021. I'm your host lisa martin. I've got a couple of guests with me here from emphasis. Alumni Yuma lacks empathy. Is back. Senior vice president and regional head of EMEA emphasis Yuma. It's great to see you welcome back to the program. >>Yeah. Hi Liza. It's great to be back for discover 2021. It's been a great opportunity to meet with a lot of our stakeholders and hp. >>Excellent. We're gonna dig into that. And so do Cutie is here as well. The CTO Cloud Advisory, VP hybrid cloud engineering platforms and automation at emphasis Sergey Welcome to the program. >>Thank you lisa. It's a pleasure to be in the program is my first time but I really enjoy it. Well >>Welcome. Welcome. So the next 15 minutes or so we're gonna unpack a survey that was just done as we know cloud has catalyzed a lot in the last year. One of those being cloud adoption. Talk to us about some of the things that you've seen as more and more enterprises are moving workloads to cloud. How is a hybrid cloud enabling businesses to grow, enabling them to actually have a competitive edge? >>Uh lisa if you uh if you look at the pre covid scenario and what there are many, many clients which actually made a significant move into cloud, but there were many few, a few of the companies who didn't really take a mature uh cloud adoption. But those companies which actually did the adoption, we see that have taken a big step with the help of the when the covid hit them because they were able to be very resilient, but at the same time they were able to the cloud adoption really help them to improve their business profits. Uh When we did this cloud reader survey across all the geography is we didn't get across the U. S. The latin, the issue pacific the email markets. And when we looked at uh what our clients and enterprises were able to recover and get all of this whole cloud adoption. We've got a number of 414 billions of profits that the enterprises can make by using this cloud adoption. And that's what we saw in this survey that we did with our clients. >>Yeah, that's huge. Enterprises the survey found can add up to you said 414 billion and that new profits annually through effective cloud adoption and sticking with you for a second. What does emphasis described as effective cloud adoption? >>When we look at cloud adoption, we have enterprises who started shifting workloads which are very comfortable for them. And then uh then they started to take the more mature understanding of moving workloads which were very critical to the business. So when we look at effective, it is a combination of both the ones that were very easy to go to the cloud, the ones that made business is able to bring in new applications and new, go to markets uh, to their segments to their clients. But then it is also about taking some of those legacy world clothes and making a choice the right choice to take it by transforming those applications and environments uh, into the cloud direction. And that's what we call as effective. It's just not the easy ones, but also those complex and legacy rebuild ones that that effectively goes on to transform itself into a new way for the for their clients and for the experience of the users. >>It's a big changes coming, big opportunities. We see, we've talked about this for many times more and more companies moving to multi cloud arrangements for a variety of reasons. What have been some of the things that emphasis has experienced and what are some of your viewpoints on a multi cloud? >>Thank you, lisa. So, um, if you look around right, you know, hybrid cloud has been the new normal. Right? And um and if you look at it, private cloud is becoming an essential component for hosting applications. You know, uh you know, when you look at it, it's more about applications which have low latency requirements, you know, it has regulatory requirements or it has a static demand of infrastructure. Now, what emphasis has done in this space is is that, you know, we have um we have developed a framework which we call it as a right loud solution framework and this is focused on implementing a hybrid multi cloud leveraging an in house developed tools and frameworks as well as platforms along with our strategic Puerto rico system, that is our biggest contribution onto the hybrid multi cloud world. Now, the foundation of our framework is emphasis Polly cloud platform. It's a unified multi cloud management platform. It can provision, it can orchestrate, it can also manage the cloud deployment across multiple of the environment. It can be a private, it can be public or it can be on the edge. Now, apart from all of these things, it also offers features and functionality is very similar to the hyper scholars and either it can be in terms of the user experience or it can be in a commercial model or a technology stack or it can be reports or it can be persona based user experience and integration with multiple systems. It brings all of these functionalities seamlessly across the multiple hybrid ecosystem. That's the biggest contribution from emphasis in this space. >>Got it. Okay. As we see the just clear growth of multi cloud in every industry. Talk to us about what the cloud radar survey uncovered with respective you mentioned that big number, the correlation between cloud transformation and profitable growth for enterprises across any industry. >>So I did mention about it uh lisa in in the previous question as well. When we looked at when we look at enterprises trying to take the cloud adoption, the big benefits for the enterprises do happen when they crossed that uh layer of moving a significant part of their existing legacy in a very transformed new world. And that brings in the new way of working for their customers for their end users and internally as well for their various stakeholders. And that I think is creating a cost structure for them, which is very, very optimal from where they were. But at the same time, it is enabling their ecosystem of of users and customers to come and operate in a very seamless fashion. And that is the biggest advantage of uh boosting profits for them at the same time, cutting costs within the, within the internal stakeholders. So at one stage you're optimizing your cost at another stage, you're bringing in the easiness for your clients to operate on, which is actually creating that enlarged profit boost. >>I'm sticking with you for a second. If we unpack that growth, that business profit growth opportunity that you the survey uncovered, Are we talking about things like faster time to market, increasing scale? What are some of the things underneath that hood? >>So, if you if you look at uh traditionally cloud was considered uh the enabler for quick, faster time to market. But now cloud has become the central theme for resilience. If you look at the covid pandemic, uh, those, those enterprises which were already cloud enabled, we're able to resiliently and sustain their business and grow their businesses. So as economy started opening up, if I can talk about an automotive client who is today enriching businesses out of china because they have the first economy that has opened up after the pandemic. So you see a lot of enablement for those enterprises which have already taken the cloud journey. And if you look at Today, enterprises are in somewhere around 17-18% of of cloud adopt mint and if they can take that to the 40%, that's when they will see that kind of boosted profits. And we can clearly see about $400 plus billion dollars of profits that enterprises can make. >>All right, so let's talk to you for a second. If we look at some of the survey results, the acceleration that is expected to be seen by in the next year of enterprises moving so many more workloads to cloud. You talked about hybrid cloud. Talk to me about how the experience of working with HP in creating joint solution suites is going to help the customers facilitate and drive that transformation. >>Thank you lisa. So if you look at H P E, H P E comes with a fine set of technology and commercial constructs, you know, that complements our right cloud framework and they offer the solutions. The whole sort of a lot of solutions offer private cloud as a service which is a major component of our right club framework. Either it is a continuous service with HP is is immoral data platform on HP hardware or video as a service based on a compose Herbal and Converse infrastructure or H. P. S cloud built on HPC cloud, build on Cray systems and all of them commercially supported with an H. P. S. Green leg offering makes it very attractive for our customers. Now, these integrations have helped us in providing a very similar metering and billing along with the chargeback solutions, very much in line with what is being provided by Hyper scholars. Apart from this, we also work very closely with H. P. E to create a very compelling sourcing strategy for driving hybrid cloud driven digital transformation while taking cost out and protecting the existing investments through various financial models for our customers, helping them in terms of transforming their digital estate in the, in the new cloud world. >>And um, I want to get your perspective as well. The HP emphasis partnership talk to me about that being a win win for your clients in every industry. >>So actually uh Visa is a great question and this probably is my third uh cube interview and I've told this previously as well in my previous interviews as well, the relationship between emphasis and hedge P is very very strategy and it's it's very very top down driven. And today we've seen very high transformative opportunities that two organizations have come together and we won't call it win win, but we call it a win win win, which is essentially win for HPV win for emphasis, but even for the clients as well. So if you look at some of the engagements that we have jointly done, everything has been transformative. I can talk about uh energy client where we've done a huge which will be D I uh engagement with them, where we have been able to take them very uh seamlessly when the covid pandemic hit them so that there are significant part of their right to users but be able to operate from their residences. I can talk about a great story about how we had enabled Green Lake for a wind energy company. Uh and how that Green Lake capability help the customer to migrate the application seamlessly uh to a hybrid cloud. And there are so many examples of similar scale and size when we look at clients in the manufacturing space and the automobile sector, where we've really done work very closely with HP across all regions and all geography is uh to make this what I would call a win win win partnership. >>I like that when when when who wouldn't want that. One more question for you talk to me about the next, as we talked about some of those survey results and I think folks can find that survey the cloud radar survey on the emphasis dot com website. I found it on the homepage there. But looking at how much Transformation is expected in the next 12 months or so, what are some of the things that we can expect from emphasis on H. P. E. to help drive and catalyze that growth that you expect to see in the next 12 months? >>Yeah. And I was talking to you before this interview and you said that yes, we gotta look at this. And I was feeling very happy that you have the opportunity to look at the side. And you said that look there's an opportunity to also make to continuously provide feedback. And we're very happy for clients to come in and look at it and do provide us the feedback. This is a constant learning for us. We have a big learning company Uh and when it comes to uh the next 12 months of agenda, I think the pipeline is very robust for both us and the hp. In terms of the way we want to take proactive transformational opportunities to the to our clients create a value differentiation on the hybrid cloud for them. And uh clearly uh this this survey clearly came back to reflect back to us that our strategy that we've done together as partners is the right strategy because there is a significant headroom for growth uh in the cloud space uh for both emphasis and H. B. >>Excellent. Well gentlemen, thank you for joining me today, talking to me about what emphasis and HP are doing together, unpacking some of the significant insights that the cloud radar survey has uncovered. We appreciate your time. >>Thank you lisa. Thank you. Thank you for giving us this opportunity. >>Absolutely. For election Soju. I'm lisa martin. You're watching the cubes coverage of HP discover 2021. Yeah, yeah.

>>Welcome >>back to HP discover 2021. My name is Dave Volonte and you're watching the cubes virtual coverage of discover 21 we're excited to welcome back our wa Kadoura, She's the vice president and worldwide go to market leader for HP. Es smoking hot. Green Lake Cloud services are welcome back to the cube. Good to see you again. >>Thank you for having me to be with you. >>So talk about how your products and services are supporting customer transformations. I'm interested in the experience that everybody has been dreaming about describe how you're giving your customer that competitive advantage. If you've got any examples, that would be awesome. >>Yeah, you got it. Um, I think as we heard Antonio say the cloud is an experience, not a destination, right? And what we're doing with Green Lake is bringing those cloud capabilities and the cloud experience to our customers. You know, we like to say co locations, data center and edge of course. So this is the cloud on prem. And so rather than forcing customers to only have to go up to cloud to get modern cloud capabilities or the benefits of things like, you know, pay as you go for consumption, et cetera. You know, cloud native capabilities like containers, leveraging, kubernetes. We now bring all of that to Green Lake and to our customers. Edge locations and co locations and data centers. We've been able to dramatically transform many of our customers businesses. Right? And you'll probably see it discover some of those examples come to life. For example, Care Stream, who is, you know, in the electronic medical imaging world, Right? They have all of the X ray equipment that capture x rays and different sort of diagnostics for patients. Um and we worked with them to not only craft a ml solution to better read and diagnose these images, um but also all of the underlying infrastructure with the HP Green Lake Ml ops platform that allows them to instantly leverage the capabilities of machine learning and the infrastructure to go with it. >>And so tell me, so, how is it resonating with customers? What you're out there, talking to customers all the time? What are they >>telling you? You know, I think what our customers appreciate about HP Green Lake is it's not sort of look, it's either all on prem in my data center and I have to fully manage it, build it, implement it, take care of it or it's fully public cloud. I have little control and basically I get whatever the public cloud gives me right. Hp Green leg gives our customers the flexibility and control that they require. Right. And so you can think of many use cases where customers have a need to have the compute storage sort of processing need to happen closer to where their data and apps live. Um and so for that exact reason our customers love the flexibility, right? Cloud one, Dato was public cloud. Cloud to Dato I think is the cloud that comes to our customers at their convenience. And to me, you know what I tell C I O S and C T O S and sort of other lines of business leaders when I meet with them is you shouldn't be forced to have to take your data and apps elsewhere to get the transformation that you need. We want to be able to bring that directly to our customers. >>Of course, a lot of the transformation is around data. We love talking about data on the cube and it's funny, I mean we talked about big data last decade. We don't use that term much anymore. Uh It was kind of overhyped but as as often times as the case may be in the early days it's overhyped but then it's under hyped when it actually starts to kick in. And I feel like we're entering a new age of data. And insights with the ascendancy of machine learning and ai what does this mean from H. P. S. Perspective and what our customers telling you that it means for them. >>Yeah no data. I think we often hear data is the new currency writes the new gold. Um You know we've heard uh Antonio even say things like look data could even become something that maybe over time companies start to put some kind of value on their balance sheet. Behind right the same way that maybe brands represented this value on a balance sheet. Um effectively what's happened with data is a lot of people have a lot of data but there's not been a lot of ability to extract insights from data. Right? And I think this is the new revolution that we're all undergoing is we finally have the modern analytics tools to actually turn the data into insights and what we bring to the table from an HPD perspective is the fact that we have the best infrastructure. We obviously now have the cloud capabilities mixed in with our data fabric or container platform, our machine learning operations platform to then be able to process that data again, integrated with many of the great I SV partners that we have on the data side allow our customers to turn that into real insights for their business. And effectively data is becoming a huge competitive advantage. Right? I think many of us are you know, leveraging some, you know, pretty interesting tools or gadgets these days, right? Like I wear one of those, you know, sleep brings, you can imagine a company like that in the future that's able to collect so much data from the folks that purchase their products. Then being able to give us insights about, you know, where is the best zip code that, you know, people get the most amount of sleep in or you know, which zip codes are the healthiest and you know, the United States or countries, et cetera. Um but data really is becoming um you know, a competitive advantage. And one of the things that we care most about at HP is also using it as a force for good and making sure that there is a sort of ethical ai capability. >>That's a great message and very important one. And and it's interesting you're saying about data and the value how well it's clearly it's clearly being valued in terms of companies market caps. I mean it's it's it's you know, maybe it's not the balance yet, but it's on the income statement in terms of data products and data services that that's happening. So we'll see if Antonio's right in the next, you know, several years. But so let's talk more about the specific data challenges that you're solving for your customers. They talk about silos, they talk about they haven't got as much value out of their data initiatives as they wanted to. What are they telling you? Are there challenges and how are you approaching it? >>Yeah, I think, you know, um data's everywhere right. The ability for customers to store the right amount of data is a huge challenge because obviously there's, you know, a huge cost associated with collecting, keeping cleansing processing, you know, all the way to sort of analyzing your data. There tends to be a ton of data silos, right? So customers are looking for a common data fabric that they can then process their data sources across and then be able to sort of tap into that data from an analytics perspective. So much of the technology again that we're focused on is be able to store the data right, Our data fabric layer with his moral right being able to process that data capture that data and then allow the analytics tools to then harness the power of that data and turn that into real business insights for our customers. Um Every customer that I have spoken to, you know whether their financial services, you know, you can imagine the big financial services. I mean they've got you know, just bazillions of pockets of data everywhere and you know, the real sort of a challenge for them is how do I build a common data platform that allows me to tap into that data in effective ways for my business users? >>You talk a little bit about how you're changing the way you're providing solutions? Maybe maybe you could contrast it with the way HP has done in the past, because I think that's important when you, when you think about, you talk a lot about green lake and as a service, but if the products are still, you know, kind of boxes and Luns and, and gigahertz and ports, then, you know, that's that's a dis continuity. So what's changed from the past and how are you feeding into the way customers are transforming their business and supporting their outcomes? >>That's exactly right. You know, at some point in time, right. If you think maybe 10 or 20 years back, it used to be very much about the infrastructure for hp. What's exciting about what we're doing differently for our customers is look, we have the best infrastructure in the business, right. Hp has been doing this you no longer than anyone has probably almost 60 years now. Um but being able to vertically integrate right, move up in that value change so that our customers can get more complete solutions is the more interesting part for our customers. Our customers love our technology, Yes, the gigahertz and the speeds and feeds all of that do matter because they, you know, make for some very powerful infrastructure. However, what makes it easier is the fact that we are building platform stacks on top of that hardware, um that help abstract away the complexity of that infrastructure and the ability to use it far more seamlessly. Um and then if you think about it, we of course have also one of the most advanced services organizations. So being able to leverage our services capabilities, our platform capabilities on top of that hardware, again, deliver it back to our customers In a consumption model, which they've become two X, which they've come to expect from a cloud model. Um and then surrounded by a very rich ecosystem of partners. And we're talking about system Integrators that now have capabilities on helping our customers run their Green Lake environments. We're talking about I. S. V. S. Right? So software stacks and platforms that fully integrate with the Green Lake platform for completely seamless solutions. Um as well as channel Partners and global distributors. So I think that's where we can truly deliver the ultimate end to end solution. It's not just the hardware, right? But it's being complemented with the right services being complemented with the right platform capabilities, the software integrations to deliver that workload that the customer expects. >>And partners, they gotta they gotta place bets, they gonna put resources time money in a line, their resources with with their their partners and their suppliers like HP. So when they ask you, hey, okay. Hp. Tell me or well, what's your overall strategy? Why is it compelling and why do you give me competitive advantage relative to some of your peers in the industry? >>Yeah, I think what, you know, partners are going to be most excited about is the openness of the platform, right? Being able to allow our partners to leverage Green Lake Central with open API so that they can integrate some of their own technologies into our platform. Uh the ability to allow them to also layer in their own um managed services on top of the platform is key. Um And of course being able to build sort of these win win solutions with the system Integrators, right. The system Integrators have some fantastic capabilities all the way from an application development, all the way down to the infrastructure management and data center delivery centers that they have. And so leveraging HP Green Lake um really helps them have access to core technologies that they need to deliver these solutions. >>I wonder if I could take a little sort of side road here and ask you because so many changes going on HP itself is transforming your customers are transforming the pandemic has accelerated all these transformations. Can you talk a little bit about how you've transformed go to market specifically in the context of of as a service? I mean that had to be quite a change for you guys. >>Yeah, no, go to market transformations in support of sort of moving from traditional go to markets right to call, go to markets or significant. Um They required us to really think through what does delivering as a service solutions mean for our direct sales force? What does it mean for our partners and their transformations and being able to support as a service solutions? Um for HP specifically, it also means um thinking about our customer outcomes, not just our ability to ship them, you know, the requisite hardware and say, look, once it's left our dog, our job is done right. It really takes our obligation all the way to the customer using the technology on a day by day basis, as well as supporting them in making sure that everything from implementation to set up to the ongoing monitoring operations of the technology is working for them in the way that they expect in an as a service way, right? We don't expect them to operate it. We don't expect them to, you know, do anything more than pick up the phone and call us if something doesn't go as planned. >>And how about your sellers and your partners? How did they respond? I mean you just wake up one day is okay guys, here we go, new compensation scheme, new way to sell new way to market that that took some thought in some time. And where are you in that journey? >>That's right. And I always say, you know, if you expect people to wake up one day and be transformed, right? You're kidding yourself. Um So everything from sort of the way that we think about our customers use cases, right? And empowering our sellers to understand the outcomes that our customers expect and demand from us to things like compensation too. You know, the partner rebate program that we leverage through the channel partners in order to give them the right incentives to also allow them to make the right investments to support Green Lake. Um, you know, we've all, you know, HP has a fairly significant field sales and solution team and so not thinking about this only as a single person that represents Green Lake, but looking at our capabilities across the board, right. We have fantastic advisory consultants on the ground with phds and data science. We have folks that understand, you know, high performance computing, so making sure that we're embedding the expertise in all of the right personas that support our customers, not just from a calm perspective, but also from an understanding of the end to end solutions that we're bringing to those markets. >>So what gets you stoked in the morning, you get out of bed and you're like, okay, I'm gonna go attack the world. What are you most excited about for H P E and his future? >>You know, it's, there's so much happening right now in this sort of cloud world. Right? Um to me the most exciting portion is the fact that given that we've now introduced on prem cloud to the world, our ability to ship new services and new capabilities, um, but also do that via a very rich partner ecosystem honestly is what probably has me most excited. This is no longer the age of go at it alone, Right. So not only are engineering and product teams hard at work in the engine room producing, you know, capabilities at sort of lightening fast speeds, but it's also our ability to partner, uh you know, whether it's with platform providers, you know, software providers or, you know, system Integrators and services providers, that ecosystem is starting to come together to deliver highly meaningful solutions to our customers and all in a very open way. Um, the number one thing that I personally care about is that our customers never feel like they are being locked in or that they are sort of being forced to have to give up certain levels of capabilities. We want to give them the best of what's out there and allow them to then have that flexibility in their solution. >>And one of the challenges, of course with virtual events is you don't have the hallway track. You know, somebody can't say, hey, have you seen that IOT zone? It's amazing. They got all these robots going around. But so what, what would you say that people should be focused on the discovery maybe things that you want to call out specific highlights or segments that you think are relevant? >>Yeah, there's gonna be a ton of fantastic stuff I think, um you know, really looking for that edge to cloud strategy, um that we're gonna be spending a lot of time talking about um looking at some of our vertical workload solutions, right. We're gonna be talking about quite a few from electronic health care records to payment solutions. Um and many more I think depending on what folks are interested in, there's gonna be something for everyone. Um Project Aurora, which now starts to announce our new security capabilities. Um, you know, the zero trust capabilities that we're delivering um is probably interesting to a lot of our customers, so lots of exciting things coming and I'm excited for our customers to check this out. >>No doubt that's a hot topic. Especially given what's been happening the news these past several months. All right, well, thanks so much for coming back in. The cube is great to see you hopefully face to face next time. >>I sure hope so. Thanks so much for having me. >>It's our pleasure. And thank you for watching and thank you for being with us and our ongoing coverage of HPD discovered 2021. This is Dave Volonte. You're watching the cube, The leader and digital tech coverage. >>Yeah.

>>Welcome back to HP discover 2021 My name is Dave Volonte and you're watching the cubes, virtual coverage of H. P. S. Big customer event. Of course, the virtual edition, we're gonna dig into transformations the role of technology in the role of senior technology leadership. Look, let's face it, H P. E. Has gone through a pretty dramatic transformation itself in the past few years. So it makes a great example in case study and with me is rashmi kumari who is the senior vice president and C. I. O. At HP rashmi welcome come on inside the cube. >>Dave Nice to be here. >>Well, it's been almost a year since Covid changed the world as we know it. How would you say the role of the CEO specifically and generally it has changed. I mean you got digital Zero Trust has gone from buzzword to >>mandate >>digital. Everybody was complacent about digital in many ways and now it's really accelerated remote work hybrid. How do you see it? >>Absolutely. As I said in the last discover that Covid has been the biggest reason to accelerate digital transformation in the company's I. C. C. I O. S role has changed tremendously in the last 15 months. It's no more just keep the operations running that's become a table stick. Our roles have become not only to create digital customer experience engaged with our customers in different ways, but also to transform the company operations from inside out to be able to give that digital experience from beginning to end off the customer engagement going forward. We have also become responsible for switching our strategies around the companies as the Covid. Covid hit in different parts of the world at different times and how companies structured their operations to go from one region to another. A global company like H. B had to look into its supply chain differently. Had to look into strategies to mitigate the risk that was created because of the supply chain disruptions as well as you go to taking care of our employees. How do you create this digital collaboration experience where teams can still come together and make the work happen for our end customers? How do we think about future employee engagement when people are not coming into these big buildings and offices and working together, But how to create the same level of collaboration coordination as well as delivery or faster uh goods and services which is enabled by technology going forward. So see I. O. And I. T. S. Role has gone from giving a different level of customer experience to a different level of employee experience as well as enabling day to day operations of the company's. Ceos have realized that digital is the way to go forward. It does not matter what industry you are in and now see a as have their seat at the table to define what the future of every company now, which is a technology company respective you are in oil and gas or mining or a technical product or a card or a mobility company. End of the day you have to act and behave like a technology company. >>So I want to ask you about that because you've you've been a Ceo and uh you know, leading technology provider now for the last three years and you've had previous roles and where you know non technical technology, you know, selling to I. T. Companies and as you point out those worlds are coming together, everybody is a technology company today. How do you think that changes the role of the C. I. O. Because it would always seem to me that there was a difference between A C. I. O. And a tech company. You know what I mean by that? And the C. I. O. It's sort of every other company is those two worlds converging. >>Absolutely. And it's interesting you pointed out that I have worked in many different industries from healthcare and pharma to entertainment to utilities. Um And now at a technology company end of the day um The issues that I. T. Deals with are pretty similar across the organization. What is different here is now my customers are people like me in other industries and I have a little bit of an advantage because just having the experience across various ecosystem. Even at H. B. Look I was fortunate um at H. B. Because of Antonio's leadership, we have topped out mandate to transform how we did business. And I talked about my next gen IT program in last year's cube interview. But at the same time while we were changing our customer partners experience from ordering to order processing to supply chain to finance. Uh We decided this pivot of becoming as a service company. And if you think about that pivot it's pretty common if it was a technology company or non technology company at HP. We were very used to selling a product and coming back three years later at the time of refresh of infrastructure or hardware. That's no more true for us now we are becoming as a service or a subscription company and I. T. Played a major role to enable that quote to cash experience. Which is very different than the traditional experience around how we stay connected with our customer, how we proactively understand their behavior. I always talk about this term. Um Digital exhaust which results into data which can result into better insight and you can not only Upsell cross l because now you have more data about your product usage, but first and the foremost give what your customer wants in a much better way because you can proactively understand their needs and wants because you are providing a digital product versus a physical product. So this is the change that most of the companies are now going through. If you look at Domino's transition, there are pills a sellers but they did better because they had better digital experience. If you look at Chipotle, these are food service companies I. K which is a furniture manufacturer across the board. We have helped our customers and industries to understand how to become a more digital provider. And and remember when uh hp says edge to cloud platform as a service edges the product, the customers who we deal with and how do we get that? Help them get their data to understand how the product is behaving and then get the information to cloud for further analysis. Um and understanding from the data that comes out of the products that gets up, >>I think you've been HP now think around three years and I've been watching of course for decades. Hp. Hp then HP is I feel like it's entering now the sort of third phase of its transformation, your phase one was okay, we gotta figure out how to deal or or operate as a separate companies. Okay. That took some time and then it was okay. Now how do we align our resources and you know, what are the waves that we're gonna ride? And how do we how do we take our human capital, our investments and what bets do we place and and all in on as a service. And now it's like okay how do we deliver on all those promises? So pretty massive transformations. You talked about edge to cloud as a service so you've got this huge pivot in your in your business. What's the technology strategy to support that transformation? >>Yeah that's a that's a great question. So as I mentioned first your second phase which was becoming a stand alone company was the next N. I. T. Program very broad and um S. Four and 60 related ecosystem application. We're even in the traditional business there was a realization that we were 100 20 billion company. We are 30 billion company. We need different types of technologies as well as more integrated across our product line across the globe. And um we I'm very happy to report that we are the last leg of next in I. T. Transformation where we have brought in new customer experience through low touch or not touch order pressing. A very strong as four capabilities. Where we are now able to run all global orders across all our hardware and services business together. And I'm happy to report that we have been able to successfully run through the transformation which a typical company of our size would take five or six years to do in around close to three years. But at the same time while we were building this foundation and the capabilities to be able to do other management, supply chain and data and analytics platforms. We also made the pivot to go to as a service now for as a service and subscription selling. It needs a very different quote to Kazakh cash experience for our customers and that's where we had to bring in um platforms like brim to do um subscription building, convergent charging and a whole different way to address. But we were lucky to have this transformation completed on which we could bolt on this new capability and we had the data and another X platform built which now these as a service products can also use to drive better insight into our customer behavior um as well as how they're using our product a real time for our operations teams. >>Well they say follow the money in the cube. We love to say follow the day to day is obviously a crucial component of competitive advantage business value. So you talk a little bit more about the role of data. I'm interested I'm interested in where I. T. Fits uh you know a lot of companies that have a Chief data officer or Ceo sometimes they're separate. Sometimes they they work you know for each other or Cdo works for C. I. O. How do you guys approach the whole data conversation? >>Yeah that's a that's a great question and has been top of the mind of a lot of C E O C I O S. Chief digital officers in many different companies. The way we have set it up here is do we do have a chief data officer and we do have a head of uh technology and platform and data within I. T. Look. The way I see is that I call the term data torture if we have multiple data lakes, if we have multiple data locations and the data is not coming together at one place at the first time that it comes out of the source system, we end up with data swamps and it's very difficult to drive insights. It's very difficult to have a single version of truth. So HP had two pronged approach. First one was as part of this next gen i. T. Transformation we embarked upon the journey first of all to define our customers and products in a very uniform way across the globe. It's called entity Master Data and Product Master Data Program. These were very very difficult program. We are now happy to report that we can understand the customer from code stage to servicing stage beginning to end across all our system. It's been a tough journey but it was a effort well spent at the same time while we were building this message capability, we also invest the time in our analytics platform because we are generating so much data now globally as one footprint. How do we link our data link to R. S. A. P. And Salesforce and all these systems where our customer data flows through and create analytics and insight from it from our customers or our operations team. At the same time, we also created a chief data officer role where the responsibility is really to drive business from understanding what decision making an analytics they need around product, around customer, around their usage, around their experience to be able to drive better alignment with our customers and products going forward. So this creates efficiencies in the organization. If you have a leader who is taking care of your platforms and data building single source of truth and you have a leader who is propagating this mature notion of handling data as enterprise data and driving that focus on understanding the metrics and the insight that the businesses need to drive better customer alignment. That's when we gain those efficiencies and behind the scenes, the chief data officer and the data leader within my organization worked very, very closely to understand each other needs sometimes out of the possible where do we need the data processing? Is it at the edge? Is it in the cloud? What's the best way to drive the technology and the platform forward? And they kind of rely on each other's knowledge and intelligence to give us give us superior results. And I have done data analytics in many different companies. This model works where you have focused on insight and analytics without because data without insight is of no value, but at the same time you need clean data. You need efficient, fast platforms to process that insight at the functional nonfunctional requirements that are business partners have and that's how we have established in here and we have seen many successes recently. As of now, >>I want to ask you a kind of a harder maybe it's not harder question. It's a weird question around single version of the truth because it's clearly a challenge for organizations and there's many applications workloads that require that single version of the truth. The operational systems, the transaction systems, the HR the salesforce. Clearly you have to have a single version of the truth. I feel like however we're on the cusp of a new era where business lines see an opportunity for whatever their own truth to work with a partner to create some kind of new data product. And it's early days in that. But I want to and maybe not the right question for HP. But I wonder if you see it with in your ecosystems where where it's it's yes, single version of truth is sort of one class of data and analytics gotta have that nail down data quality, everything else. But then there's this sort of artistic version of the data where business people need more freedom. They need more latitude to create. Are you seeing that? And maybe you can help me put that into context. >>Uh, that's a great question. David. I'm glad you asked it. So I think tom Davenport who is known in the data space talks about the offensive and the defensive use cases of leveraging data. I think the piece that you talked about where it's clean, it's pristine, it's quality. It's all that most of those offer the offensive use cases where you are improving company's operations incrementally because you have very clean that I have very good understanding of how my territories are doing, how my customers are doing how my products are doing. How am I meeting my sls or how my financials are looking? There's no room for failure in that area. The other area is though, which works on the same set of data. It's not a different set of data, but the need is more around finding needles in the haystack to come up with new needs, new ones and customers or new business models that we go with. The way we have done it is we do take this data take out what's not allowed for everybody to be seen and then what we call is a private space. But that's this entire data available to our business leader, not real time because the need is not as real time because they're doing more what we call this predictive analytics to be able to leverage the same data set and run their analytics. And we work very closely with business in its we educate them. We tell them how to leverage this data set and use it and gather their feedback to understand what they need in that space to continue to run with their with their analytics. I think as we talk about hindsight insight and foresight hindsight and insight happens more from this clean data lakes where you have authenticity, you have quality and then most of the foresight happens in a different space where the users have more leverage to use data in many different ways to drive analytics and insights which is not readily available. >>Thank you for that. That's interesting discussion. You know digital transformation. It's a journey and it's going to take many years. A lot of ways, not a lot of ways 2020 was a forced March to digital. If you weren't a digital business, you were out of business and you really didn't have much time to plan. So now organizations are stepping back saying, okay let's really lean into our strategy the journey and along the way there's gonna be blind spots, there's bumps in the road when you look out what are the potential disruptions that you see maybe in terms of how companies are currently approaching their digital transformations? That's a great question. >>Dave and I'm going to take a little bit more longer term view on this topic. Right in what's top of my mind um recently is the whole topic of E. S. G. Environmental, social and governance. Most of the companies have governance in place, right? Because they are either public companies or they're under some kind of uh scrutiny from different regulatory bodies or what not. Even if you're a startup, you need to do things with our customers and what not. It has been there for companies. It continues to be there. We the public companies are very good at making sure that we have the right compliance, right privacy, right governance in in in place. Now we'll talk about cyber security. I think that creates a whole new challenge in that governance space. However, we have the set up within our companies to be able to handle that challenge. Now, when we go to social, what happened last year was really important. And now as each and every company, we need to think about what are we doing from our perspective to play our part in that. And not only the bigger companies leaders at our level, I would say that Between last March and this year, I have hired more than 400 people during pandemic, which was all virtual, but me and my team have made sure that we are doing the right thing to drive inclusion and diversity, which is also very big objective for h P E. And Antonio himself has been very active in various round tables in us at the world Economic forum level and I think it's really important for companies to create that opportunity, remove that disparity that's there for the underserved communities. If we want to continue to be successful in this world too, create innovative products and services, we need to sell it to the broader cross section of populations and to be able to do that, we need to bring them in our fold and enable them to create that um, equal consumption capabilities across different sets of people. Hp has taken many initiatives and so are many companies. I feel like uh, The momentum that companies have now created around the topic of equality is very important. I'm also very excited to see that a lot of startups are now coming up to serve that 99% versus just the shiny ones, as you know, in the bay area to create better delivery methods of food or products. Right. The third piece, which is environmental, is extremely important as well as we have seen recently in many companies and where even the dollar or the economic value is flowing are around the companies which are serious about environmental HP recently published its living Progress report. We have been in the forefront of innovation to reduce carbon emissions, we help our customers, um, through those processes. Again, if we do, if our planet is on fire, none of us will exist, right. So we all have to do that every little part to be able to do better. And I'm happy to report, I myself as a person, solar panels, battery electric cars, whatever I can do, but I think something more needs to happen right where as an individual I need to pitch in, but maybe utilities will be so green in the future that I don't need to put panels on my roof, which again creates a different kind of uh waste going forward. So when you ask me about disruptions, I personally feel that successful company like ours have to have E. S. G. Top of their mind and think of products and services from that perspective, which creates equal opportunity for people, which creates better environment sustainability going forward. And, you know, our customers are investors are very interested in seeing what we are doing to be able to serve that cause uh for for bigger cross section of companies, and I'm most of the time very happy to share with my C I. O cohort around how are H. P E F s capabilities creates or feeds into the circular economy, how much e waste we have recycled or kept it off of landfills are green capabilities, How it reduces the evils going forward as well as our sustainability initiatives, which can help other, see IOS to be more um carbon neutral going forward as well. >>You know, that's a great answer, rashmi, thank you for that because I gotta tell you hear a lot of mumbo jumbo about E S G. But that was a very substantive, thoughtful response that I think, I think tech companies in particular are have to lead in our leading in this area. So I really appreciate that sentiment. I want to end with a very important topic which is cyber. It's obviously, you know, escalated in, in the news the last several months. It's always in the news, but You know, 10 or 15 years ago there was this mentality of failure equals fire. Now we realize, hey, they're gonna get in, it's how you handle it. Cyber has become a board level topic, you know? Years ago there was a lot of discussion, oh, you can't have the sec ops team working for the C. I. O. Because that's like the Fox watching the Henhouse, that's changed. Uh it's been a real awakening, a kind of a rude awakening. So the world is now more virtual, you've gotta secure physical uh assets. I mean, any knucklehead can now become a ransomware attack, er they can, they can, they can buy ransomware as a services in the dark, dark web. So that's something we've never seen before. You're seeing supply chains get hacked and self forming malware. I mean, it's a really scary time. So you've got these intellectual assets, it's a top priority for organizations. Are you seeing a convergence of the sea? So roll the C. I. O. Roll the line of business roles relative to sort of prior years in terms of driving security throughout organizations. >>This is a great question. And this was a big discussion at my public board meeting a couple of days ago. It's as as I talk about many topics, if you think digital, if you think data, if you think is you, it's no more one organizations, business, it's now everybody's responsibility. I saw a Wall Street Journal article a couple of days ago where Somebody has compared cyber to 9-11-type scenario that if it happens for a company, that's the level of impact you feel on your on your operations. So, you know, all models are going to change where C so reports to see IO at H P E. We are also into products or security and that's why I see. So is a peer of mine who I worked with very closely who also worked with product teams where we are saving our customers from a lot of pain in this space going forward. And H. B. E. Itself is investing enormous amount of efforts in time in coming out of products which are which are secured and are not vulnerable to these types of attacks. The way I see it is see So role has become extremely critical in every company and the big part of that role is to make people understand that cybersecurity is also everybody's responsibility. That's why in I. T. V. Propagate def sec ups. Um As we talk about it, we are very very careful about picking the right products and services. This is one area where companies cannot shy away from investing. You have to continuously looking at cyber security architecture, you have to continuously look at and understand where the gaps are and how do we switch our product or service that we use from the providers to make sure our companies stay secure The training, not only for individual employees around anti phishing or what does cybersecurity mean, but also to the executive committee and to the board around what cybersecurity means, what zero trust means, but at the same time doing drive ins, we did it for business continuity and disaster recovery. Before now at this time we do it for a ransomware attack and stay prepared as you mentioned. And we all say in tech community, it's always if not when no company can them their chest and say, oh, we are fully secured because something can happen going forward. But what is the readiness for something that can happen? It has to be handled at the same risk level as a pandemic or earthquake or a natural disaster. And assume that it's going to happen and how as a company we will behave when when something like this happen. So I'm here's believer in the framework of uh protect, detect, govern and respond um as these things happen. So we need to have exercises within the company to ensure that everybody is aware of the part that they play day today but at the same time when some event happen and making sure we do very periodic reviews of I. T. And cyber practices across the company. There is no more differentiation between I. T. And O. T. That was 10 years ago. I remember working with different industries where OT was totally out of reach of I. T. And guess what happened? Wanna cry and Petra and XP machines were still running your supply chains and they were not protected. So if it's a technology it needs to be protected. That's the mindset. People need to go with invest in education, training, um awareness of your employees, your management committee, your board and do frequent exercises to understand how to respond when something like this happen. See it's a big responsibility to protect our customer data, our customers operations and we all need to be responsible and accountable to be able to provide all our products and services to our customers when something unforeseen like this happens, >>Russian, very generous with your time. Thank you so much for coming back in the CUBA is great to have you again. >>Thank you. Dave was really nice chatting with you. Thanks >>for being with us for our ongoing coverage of HP discover 21 This is Dave Volonte, you're watching the virtual cube, the leader in digital tech coverage. Be right back. >>Mm hmm, mm.

>>Welcome back to HPD discovered 2021. The virtual edition. My name is Dave Volonte and you're watching the cube. We're here with Omar assad is the vice president GM of H P S H C I and primary storage and data management business. And Sandeep Singh was the vice president of marketing for HP storage division. Welcome gents. Great to see you. >>Great to be here. Dave, >>It's a pleasure to be here today. >>Hey, so uh, last month you guys, you made a big announcement and and now you're, you know, shining the spotlight on that here at discover Cindy. Maybe you can give us a quick recap, what do we need to know? >>Yeah, Dave. We announced that we're expanding HB Green Lake by transforming HB storage to a cloud native software defined data services business. We unveiled a new vision for data that accelerates data, dream of transformation for our customers. Uh and it introduced a and we introduced the data services platform that consists of two game changing innovations are first announcement was Data services cloud console. It's a SAS based console that delivers the cut operational agility and it's designed to unify data operations through a suite of cloud data services. Our 2nd announcement is HPE. Electra. It's cloud native data infrastructure to power your data edge to cloud. And it's managed natively with data services cloud console to bring that cloud operational model to our customers wherever their data lives together with the data services platform. Hp Green Green Lake brings that cloud experience to our customers data across edge and on premises environment and lays the foundation for our customers to shift from managing storage to managing data. >>Well, I think it lays the foundation for the next decade. You know, when we entered this past decade, we we were Ricky bobby's terms like software led that that sort of morphed into. So the software defined data center containers with kubernetes, Let's zoom out for a minute. If we can homer maybe you could describe the problems that you're trying to address with this announcement. >>Thanks dave. It's always a pleasure talking to you on these topics. So in my role as general manager for primary storage, I speak with the hundreds of customers across the board and I consistently hear that data is at the heart of what our customers are doing and they're looking for a data driven transformative approach to their business. But as they engage on these things, there are two challenges that they consistently faced. The first one is that managing storage at scale Is rife with complexity. So while storage has gotten faster in the last 20 years, managing a single array or maybe two or three arrays has gotten simpler over time. But managing storage at scale when you deploy fleet. So storage as customers continue to gather, store and lifecycle that data. This process is extremely frustrating for customers. Still I. T. Administrators are firefighting, they're unable to innovate for their business because now data spans all the way from edge to corridor cloud. And then with the advent of public cloud there's another dimension of multi cloud that has been added to their data sprawl. And then secondly what what we what we consistently hear is that idea administrators need to shift from managing storage to managing data. What this basically means is that I. D. Has a desire to mobilize, protect and provision data seamlessly across its lifecycle and across the locations that it is stored at. Uh This ensures that I. D. Leaders uh and also people within the organization understand the context of the data that they store and they operate upon. Yet data management is an extremely big challenge and it is a web of fragmented data silos across processes across infrastructure all the way from test and dev to administration uh to production uh to back up to lifecycle data management. Uh And so up till now data management was tied up with storage management and this needs to change for our customers especially with the diversity of the application workloads as they're growing and as customers are expanding their footprint across a multi cloud environment >>just to add to almost uh response there. We recently conducted a survey that was actually done by E. S. She. Um and that was a survey of IT. decision makers. And it's interesting what it showcased, 93% of the respondents indicated that storage and data management complexity is impeding their digital transformation. 95% of the respondents indicated that solving storage and data management complexity is a top 10 business initiative for them and 94% want to bring the cloud experience on premises, >>you know, al china. And I think as you guys move to the sort of software world and container world affinity to developers homer, you talked about, you know, things like data protection and we talk about security being bolted on all the time. Now. It's designed in it's it's done at sort of the point of creation, not as an afterthought. And that's a big change that we see coming. Uh But let's talk about, you know, what also needs to change as customers make the move from this idea of managing storage to to managing data or maybe you can take that one. >>That's a that's a that's a very interesting problem. Right. What are the things that have to be true in order for us to move into this new data management model? So, dave one of the things that the public cloud got right is the cloud operational model uh which sets the standard for agility and a fast pace for our customers in a classic I. T. On prime model, if you ever wanted to stand up an application or if you were thinking about standing up a particular workload, uh you're going to file a series of I. T. Tickets and then you're at the mercy of whatever complex processes exist within organization and and depending on what the level of approvals are within a particular organization, standing up a workload can take days, weeks or even months in certain cases. So what cloud did was they brought that level of simplicity for someone that wanted to instead she ate an app. This means that the provisioning of underlying infrastructure that makes that workload possible needs to be reduced to minutes from days and weeks. But so what we are intending to do over here is to bring the best of both worlds together so that the cloud experience can be experienced everywhere with ease and simplicity and the customers don't need to change their operating model. So it's blending the two together. And that's what we are trying to usher in into this new era where we start to differentiate between data management and storage management as two independent things. >>Great, thank you for that. Omer sometimes I wonder if you could share with the audience, you know, the vision that you guys unveiled, What does it look like? How are you making it actually substantive and and real? >>Yeah. Dave. That's also great question. Um across the board it's time to reimagine data management. Everything that homer shared. Those challenges are leading to customers needing to break down the silos and complexity that plagues these distributed data environments. And our vision is to deliver a new data experience that helps customers unleash the power of data. We call this vision unified data jobs, Unified Data Ops integrates data centric policies to streamline data management, cloud native control to bring the cloud operational model to where customers data labs and a I driven insights to make the infrastructure invisible. It delivers a new data experience to simplify and bring that agility of cloud to data infrastructure. Streamline data management and help customers innovate faster than ever before. We're making the promise of Unified Data Ops Real by transforming Hve storage to a cloud native software defined data services business and introducing a data services platform that expands Hve Green Lake. >>I mean, you know, you talk about the complexity, I see, I look at it as you kind of almost embracing the complexity saying, look, it's gonna keep getting more complex as the cloud expands to the edge on prem Cross cloud, it gets more complex underneath. What you're doing is you're almost embracing that complexity and putting a layer over it and hiding that complexity from from the end customer that and so they can spend their time doing other things over. I wonder if you can maybe talk a little bit more about the data services console, Is it sort of another software layer to manage infrastructure? What exactly is it? >>It's a lot more than that, Dave and you're you're 100% right. It's basically we're attempting in this release to attack that complexity head on. So simply put data services. Cloud console is a SAS based console that delivers cloud operational model and cloud operational agility uh to our customers. It unifies data operations through a series of cloud data services that are delivered on top of this console to our customers in a continuous innovation stream. Uh And what we have done is going back to the point that I made earlier separating storage and data management and putting the strong suites of each of those together into the SAS delivered console for our customers. So what we have done is we have separated data and infrastructure management away from physical hardware to provide a comprehensive and a unified approach to managing data and infrastructure wherever it lives. From a customer's perspective, it could be at the edge, it could be in a coal. Oh, it could be in their data center or it could be a bunch of data services that are deployed within the public cloud. So now our customers with data services. Cloud console can manage the entire life cycle of their data from all the way from deployment, upgrading and optimizing it uh from a single console from anywhere in the world. Uh This console is designed to streamline data management with cloud data services that enable access to data. It allows for policy-based data protection, it allows for an organizational wide search on top of your storage assets. And we deliver basically a 360° visibility to all your data from a single console that the customer can experience from anywhere. So, so if you look at the journey the way we're deciding to deliver this. So the first, in its first incarnation, uh Data services, Cloud console gives you infrastructure and cloud data services to start to do data management along with that. But this is that foundation that we are placing in front of our customers, the SAS console, through which we get touch our customers on a daily basis. And now as our customers get access to the SAAS platform on the back end, we will continue to roll in additional services throughout the years on a true SAS based innovation base for our customers. And and these services can will be will be ranging all the way from data protection to multiple out data management, all the way to visibility all the way to understanding the context of your data as it's stored across your enterprise. And in addition to that, we're offering a consistent revised unified Api which allows for our customers to build automation against their storage infrastructure. Without ever worrying about that. As infrastructure changes, uh, the A. P I proof points are going to break for them. That is never going to happen because they are going to be programming to a single SAS based aPI interface from now on. >>Right. And that brings in this idea of infrastructure as code because you talk about as a service to talk about Green Lake and and my question is always okay. Tell me what's behind that. And if and if and if and if you're talking about boxes and and widgets, that's a it's a problem. And you're not, you're talking about services and A P. I. S and microservices and that's really the future model and infrastructure is code and ultimately data as code is really part of that. So, All right. So you guys, I know some of your branding folks, you guys give deep thought to this. So the second part of the announcement is the new product brands and deep maybe you can talk about that a little bit. >>Sure. Ultimately delivering the cloud operational model requires cognitive data infrastructure and that has been engineered to be natively managed from the cloud. And that's why we have also introduced H. P. E. Electra. Omar, Can you perhaps described HB electro even more. >>Absolutely. Thank you. Sandy. Uh, so with with HB Electoral we're launching a new brand of cloud native hardware infrastructure to power our customers data all the way from edge to the core to the cloud. The releases are smaller models for the edge then at the same time having models for the data center and then expanding those services into the public cloud as well. Right. All these hardware devices, Electoral hardware devices are cloud native and powered by our data services. Cloud Council, we're announcing two models with this launch H. P. E Electoral 9000. Uh, this is for our mission critical workloads. It has its history and bases in H P E. Primera. It comes with 100% availability guarantee. Uh It's the first of its type in the industry. It comes with standard support contract, no special verb is required. And then we're also launching HB Electoral 6000. Uh These are based in our history of uh nimble storage systems. Uh These these are for business critical applications, especially for that mid range of the storage market, optimizing price, performance and efficiency. Both of these systems are full envy any storage powered by our timeless capabilities with data in place upgrades. And then they both deliver a unified infrastructure and data management experience through the data services, cloud console. Uh And and and at the back end unified Ai Ops experience with H P. E. Info site is seamlessly blended in along with the offering for our >>customers. So this is what I was talking about before. It's sort of not your grandfather's storage business anymore. This is this is this is something that is part of that, that unified vision, that layer that I talked about, the A. P. I. Is the program ability. So you're you're reaching into new territory here. Maybe you can give us an example of how the customers experience what that looks like. >>Excellent. Love to Dave. So essentially what we're doing is we're changing the storage experience to a true cloud operational model for our customers. These recent announcements that we just went through along with, indeed they expand the cloud experience that our customers get with storage as a service with HP Green Lake. So a couple of examples to make this real. So the first of all is simplified deployment. Uh So I t no longer has to go through complex startup and deployment processes. Now all you need to do is these systems shipped and delivered to the customer's data center. Operational staff just need to rack and stack and then leave connect the power cable, connect the network cable. And the job is done. From that point onwards, data services console takes over where you can onboard these systems, you can provision these systems if you have a pre existing organization wide security as well as standard profile setup in data services console, we can automatically apply those on your behalf and bring these systems online. From a customer's perspective, they can be anywhere in the world to onboard these systems, they could be driving in a car, they could be sitting on a beach. Uh And and you know, these systems are automatically on boarded through this cloud operational model which is delivered through the SAAS application for our customers. Another big example. All that I'd like to shed light on is intent based provisioning. Uh So Dave typically provisioning a workload within a data center is an extremely spreadsheet driven trial and error kind of a task. Which system do I land it on? Uh Is my existing sl is going to be affected which systems that loaded which systems are loaded enough that I put this additional workload on it and the performance doesn't take. All of these decisions are trial and error on a constant basis with cloud Data services console along with the electron new systems that are constantly in a loop back information feeding uh Typical analytics to the console. All you need to do is to describe the type of the workload and the intent of the workload in terms of block size S. L. A. That you would like to experience at that point. Data services console consults with intra site at the back end. We run through thousands of data points that are constantly being given to us by your fleet and we come back with a few recommendations. You can accept the recommendation and at that time we go ahead and fully deploy this workload on your behalf or you can specify a particular system and then people try to enforce the S. L. A. On that system. So it completely eliminates the guesswork and the planning that you have to do in this regard. Uh And last but not the least. Uh You know, one of the most important things is, you know, upgrades has been a huge problem for our customers. Uh And typically oftentimes when you're not in this constant, you know, loop back communication with your customers. It often is a big challenge to identify which release or which bug fix or which update goes on to which particular machine, all of that has been completely taken away from our customers and fully automated. Uh We run thousands of signatures across are installed base. We identify which upgrades need to be curated for which machines in a fleet for a particular customer. And then if it applies to that customer we presented, and if the customer accepts it, we automatically go ahead and upgrade the system and and and last, but not the least from a global management perspective. Now, a customer has an independent data view of their data estate, independent from a storage estate and data services. Council can blend the two to give a consistent view or you can just look at the fleet view or the data view. >>It's kind of the holy Grail. I mean I've been in this business a long time and I think I. T. People have dreamt about you know this kind of capability for for a long long time. I wonder if we could sort of stay on the customers for a moment here and and talk about what's enabled. Now. Everybody's talking digital transformation. I joke about the joke. Not funny. The force marched to digital with Covid. Uh and we really wasn't planned for but the customers really want to drive now that digital transfer some of them are on the back burner and now they're moving to the front burner. What are the outcomes that are that are enabled here? Omar. >>Excellent. So so on on a typical basis for a traditional I. T. Customer this cloud operational model means that you know information technology staff can move a lot faster and they can be a lot more productive on the things that are directly relevant to their business. They can get up to 99% of the savings back to spend more time on strategic projects or best of all spend time with their families rather than managing and upgrading infrastructure and fleets of infrastructure. Right for line of business owners, the new experience means that their data infrastructure can be presented can be provision where the self service on demand type of capability. Uh They necessarily don't have to be in the data center to be able to make those decisions. Capacity management, performance management, all of that is died in and presented to them wherever they are easy to consume. SaS based models and especially for data innovators, whether it's D B A s, whether it's data analysts, they can start to consume infrastructure and ultimately data as a code to speed up their app development because again, the context that we're bringing forward is the context of data decoupling it from. Actually, storage management, storage management and data management are now two separate domains that can be presented through a single console to tie the end to end picture for a customer. But at the end of the day, what we have felt is that customers really, really want to rely and move forward with the data management and leave infrastructure management to machine oriented task, which we have completely automated on their behalf. >>So I'm sure you've heard you got the memo about, you know, H H p going all in on as a service. Uh it is clear that the companies all in. How does this announcement fit in to that overall mission? Cindy >>dave We believe the future is edge to cloud and our mission is to be the edge to cloud platform as a service company and as as HB transforms HP Green Lake is our unified cloud platform. Hp Green Link is how we deliver cloud services and agile cloud experiences to customers applications and data across the edge to cloud. With the storage announcement that we made recently, we announced that we're expanding HB Green Lake with as a service transformation of the HPV storage business to a cloud native software defined data services business. And this expands storage as a service, delivering full cloud experience to our customers data across edge and on prem environment across the board were committed to being a strategic partner for every one of our customers and helping them accelerate their digital transformation. >>Yeah, that's where the puck is going guys. Hey as always great conversation with with our friends from HP storage. Thanks so much for the collaboration and congratulations on the announcements and and I know you're not done yet. >>Thanks. Dave. Thanks. Dave. >>Thanks. Dave. It's a pleasure to be here. >>You're very welcome. And thank you for being with us for hp. You discovered 2021 you're watching the cube, the leader digital check coverage. Keep it right there, but right back. >>Yeah. Yeah.